Machine-learning framework for coordinating and optimizing healthcare resource utilization and delivery of healthcare services across an integrated healthcare system

ABSTRACT

Techniques are described optimizing operations of an integrated healthcare system in real-time using a machine learning framework. In one embodiment, a method comprises monitoring, by a system operatively coupled to a processor, activity of healthcare workers of a healthcare system over a defined timeframe in association with operation of the healthcare system, including monitoring performance of healthcare tasks scheduled for performance over the defined timeframe. The method further comprises determining, by the system based on the monitoring, a timeslot within the defined timeframe in which a healthcare worker of the healthcare workers is not performing, anticipated to perform, or scheduled to perform a healthcare task of the healthcare tasks, and determining, by the system, a supplemental healthcare task for performance by the healthcare worker during the timeslot.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of priorityto U.S. patent application Ser. No. 16/456,325 filed Jun. 28, 2019 andtitled “MACHINE-LEARNING FRAMEWORK FOR COORDINATING AND OPTIMIZINGHEALTHCARE RESOURCE UTILIZATION AND DELIVERY OF HEALTHCARE SERVICESACROSS AN INTEGRATED HEALTHCARE SYSTEM,” the entirety of whichapplication is hereby incorporated herein by reference.

TECHNICAL FIELD

This application relates to computer-implemented techniques forcoordinating and optimizing healthcare resource utilization and deliveryof healthcare services across an integrated healthcare system using amachine learning framework.

BACKGROUND

The predominant form of health care available in the United States isfragmented care, provided by atomistic, unconnected physician practicesand a culture of physician autonomy, hospitals, and other providers.Fragmentation in healthcare delivery refers to the systemic misalignmentof incentives or lack of coordination among providers that results ininefficient allocation of resources in a manner that hinders patientcare. For example, today, an individual must make the decision to seekmedical advice or attention with little or no guidance as to how andwhere to initiate receiving the appropriate care based on the patient'sassumed medical need. While advice may be sought through a friend orfamily member or searching the Internet, entry into the formalhealthcare ecosystem is dependent on the available capacity at eachentry point and is not coordinated across multiple sites or modalities.

Once an initial consultation or assessment is performed by acredentialed healthcare provider (entry point), a plan of action isgenerally developed that involves a series of recommended actions orservices to be rendered to the individual before re-assessment iscompleted and additional actions or services are prescribed. This cycleof assessment and assignment or prescription of actions or services tobe rendered may be repeated multiple times. However, each of theseactions or services are facilitated or rendered by disparate,uncoordinated, operating entities which are composed of siloed people,processes, and technology. Each operating entity has a finite amount ofcapacity that makes up their available capacity to render various levelsof service or activities. As a result, the individual seeking theseservices or activities is reliant on each individual operating entity'sspecifically assigned resources dictating available capacity with littleor no regard to the specific needs and preferences of the individual.Never are all operating entities and patients coordinated in ameaningful fashion to deliver optimal operating efficiency utilizing allavailable resources in a network to drive an optimized, and patientspecific experience according to patient preference.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements or delineate any scope of thedifferent embodiments or any scope of the claims. Its sole purpose is topresent concepts in a simplified form as a prelude to the more detaileddescription that is presented later. In one or more embodimentsdescribed herein, systems, computer-implemented methods, apparatusand/or computer program products are described that provide forcoordinating and optimizing healthcare resource utilization and deliveryof healthcare services across an integrated healthcare system using amachine learning framework.

According to an embodiment, a system is provided that comprises a memorythat stores computer executable components, and a processor thatexecutes the computer executable components stored in the memory. Thecomputer executable components can comprise a task informationextraction component that receives information identifying currentlypending healthcare tasks for performance by healthcare workers of ahealthcare system. The computer executable components further comprisean activity monitoring component that monitors activity of thehealthcare workers in association with operation of the healthcaresystem, and an availability analysis component that determinesavailability information regarding current availability of respectivehealthcare workers of the healthcare workers to perform the currentlypending tasks based on the activity information. The computer executablecomponents further comprise a task optimization analysis component thatdetermines a task assignment scheme that assigns one or more of thehealthcare workers to the currently pending tasks in a manner thatminimizes a total amount of delay between timing of origination of thecurrently pending tasks and timing of initiation of performance of thecurrently pending tasks based on the availability information.

In one or more embodiments, the availability information indicatesdurations of time until respective healthcare workers of the healthcareworkers can initiate the performance of the currently pending tasks. Insome implementations, the availability analysis component determines thedurations of time based on locations of the respective healthcareworkers and task locations of the respective tasks. The availabilityanalysis component can also determine the availability information basedon known scheduling information regarding known or expected times ortimeframes in which the respective healthcare workers will beunavailable (e.g., off-schedule or otherwise performing a task), basedon expected durations of time to complete tasks underway, based oncurrent operating conditions of the healthcare system and the like. Invarious embodiments, the availability analysis component can employmachine learning and artificial intelligence to facilitate determiningthe availability information (e.g., using one or more modelsdeveloped/trained based on historical activity information for thehealthcare workers and historical performance of the healthcare tasksunder various operating conditions/contexts of the healthcare system).

In some implementations, the task optimization analysis componentfurther determines the task assignment scheme based on defined workercapability information for the respective healthcare workers and definedcapability requirements of respective healthcare tasks of the currentlypending task. With these implementations, the computer executablecomponents further comprise a filtering component that identifiessubsets of the healthcare workers capable or credentialed to perform therespective healthcare tasks based on the defined worker capabilityinformation and the defined capability requirements, and wherein thetask optimization analysis component restricts assignment of the one ormore healthcare workers to the currently pending tasks based on thesubsets. For example, the defined worker capability information canidentify different types of healthcare tasks the respective healthcareworkers are capable of performing. The defined worker capabilityinformation can also comprise preference classifications for thedifferent types of healthcare tasks representative of relativepreference for performance of the different types of healthcare tasks bythe respective healthcare workers, and wherein the task optimizationanalysis component further determines the task assignment scheme basedon the preference classifications. In some implementations, the taskoptimization analysis component further determines the task assignmentscheme using an optimization function that favors respective healthcaretasks of the currently pending healthcare tasks to primary healthcareworkers of the healthcare workers that have a higher preferenceclassification for the currently pending tasks relative to secondaryhealthcare workers of the healthcare workers that have a lowerpreference classification for the currently pending tasks based onrespective types of the currently pending tasks.

The task optimization analysis component can also be configured todetermine the task assignment scheme based on priority informationassociated with respective tasks of the currently pending tasks thatidentifies priority levels of the respective tasks. The taskoptimization analysis component can also evaluate costs associated withdifferent task assignment schemes that assign the one or more healthcareworkers to the currently pending tasks in different manners and selectsthe task assignment scheme based on the task assignment schememinimizing the costs. For example, the costs can include financial costsattributed to time delays, attributed to compensation schemes associatedwith using certain workers for certain tasks, attributed to expectedclinical complications or losses attributed to using certain workers forcertain tasks, costs attributed to failure to meet defined regulatoryrequirements/protocols associated with performing the respective tasks(e.g., by a qualified healthcare worker, in a minimum timeframe, etc.)and the like. In some embodiments, the currently pending healthcaretasks involve provision of healthcare to a patient, and wherein the taskoptimization analysis component determines the task assignment schemebased on one or more preferences of the patient.

In various embodiments, the task optimization analysis component furtherdetermines the task assignment scheme using one or more machine learningtask optimization models configured to infer optimal tasks forperformance by the healthcare workers based on analysis of historicaloperations data regarding historical performance of various healthcaretasks by the healthcare workers under different operating conditions ofthe healthcare system. The computer executable components can alsocomprise a task forecasting component that forecasts upcoming need ofthe respective healthcare workers in association with performance offuture tasks of the healthcare system within a defined, upcomingtimeframe, and wherein the task assignment component further determinesthe task assignment scheme based on the upcoming need.

In another embodiment, another system is provided that comprises amemory that stores computer executable components, and a processor thatexecutes the computer executable components stored in the memory. Thecomputer executable components can comprise an activity monitoringcomponent that monitors activity of healthcare workers of a healthcaresystem over a defined time period in association with operation of thehealthcare system, including monitoring performance of healthcare tasksscheduled for performance over the defined time period. The computerexecutable components further comprise a availability analysis componentthat determines, based on the monitoring, a timeslot within the definedtime period in which a healthcare worker of the healthcare workers isnot performing, anticipated to or scheduled to perform a healthcare taskof the healthcare tasks, and a task optimization analysis component thatdetermines a supplemental healthcare task for performance by thehealthcare worker during the timeslot.

In some embodiments, the computer executable components further comprisea task assignment component that generates and sends a task assignmentmessage to a device associated with the healthcare worker comprisinginformation that recommends the healthcare worker perform thesupplemental healthcare task during the timeslot. In one implementationof these embodiments, the computer executable components furthercomprise a task compensation evaluation component that determines amonetary compensation value for performance of the supplementalhealthcare task by the healthcare worker based on a qualification of thehealthcare worker, a type of the task, and an expected duration of thetask, and wherein the task assignment message comprises informationidentifying the monetary compensation value. In various implementations,the supplemental task comprises a telemedicine service, and wherein thetask assignment component includes a link associated with thetelemedicine service, wherein selection of the link facilitatesperformance of the telemedicine service using the device.

The computer executable components can further comprise a locationtracking component that receives location and movement data regardinglocations and movement of the healthcare workers in real-time over thedefined time period, and wherein the task monitoring componentdetermines the timeslot based on the location and movement data. In oneimplementation, the timeslot comprises a traveling timeslot over whichthe healthcare worker will travel from a first location to a secondlocation, and wherein the task optimization analysis componentdetermines the supplemental task based on a mode the travel. In someembodiments, the availability analysis component further determines,based on the monitoring, one or more segments of idle time associatedwith a current healthcare task of the healthcare tasks that is currentlybeing performed by the healthcare worker, and wherein the taskoptimization analysis component determines another supplementalhealthcare task for performance by the healthcare worker during the oneor more segments of idle time. For example, the idle time can include atime associated with a healthcare task that be used to multitask, suchas time between seeing patients, time while waiting for laboratoryresults, and the like, in which the healthcare worker can perform atelemedicine task or review documents using mobile device.

In some embodiments, the task optimization analysis component can alsodetermine the supplemental healthcare task based on a location of thehealthcare worker and a duration of the timeslot. The task optimizationanalysis component can also determine the supplemental healthcare taskbased on a current operating context of the healthcare system. Inanother implementation in which the supplemental healthcare taskinvolves provision of healthcare to a patient, the task optimizationanalysis component can also determine the supplemental healthcare taskfor performance by the healthcare worker based on one or morepreferences of the patient. The task optimization analysis component canalso determine the supplemental healthcare task based on one or morepreferences of the healthcare worker. In various implementations, thetask optimization analysis component can determine the supplementalhealthcare task using one or more machine learning task optimizationmodels configured to infer optimal tasks for performance by thehealthcare workers based on analysis of historical operations dataregarding historical performance of various healthcare tasks by thehealthcare workers under different operating conditions of thehealthcare system.

In some embodiments, elements described in the disclosed systems can beembodied in different forms such as a computer-implemented method, acomputer program product, or another form.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example, non-limiting systemfor coordinating and optimizing resource utilization and delivery ofhealthcare services across an integrated healthcare system using amachine learning framework, in accordance with one or more embodimentsof the disclosed subject matter.

FIG. 2 illustrates example healthcare information systems and sourcesthat can provide information that facilitates coordinating andoptimizing resource utilization and delivery of healthcare servicesacross an integrated healthcare system using a machine learningframework, in accordance with one or more embodiments of the disclosedsubject matter.

FIG. 3 presents an example task assessment module that facilitatesdetermining information regarding currently pending and forecastedhealthcare tasks for performance by one or more operating entities of anintegrated healthcare system in accordance with one or more embodimentsof the disclosed subject matter.

FIG. 4 presents example indexed task data generated by the taskassessment module in accordance with one or more embodiments of thedisclosed subject matter.

FIG. 5 presents an example resource assessment module that facilitatesdetermining information regarding availability of resources of anintegrated healthcare system in accordance with one or more embodimentsof the disclosed subject matter.

FIG. 6 presents example resource availability data regardingavailability of resources of an integrated healthcare system inaccordance with one or more embodiments of the disclosed subject matter.

FIG. 7 illustrates an example task scheduling and resource assignmentoptimization module that facilitates coordinating and optimizingresource utilization and delivery of healthcare services across anintegrated healthcare system in accordance with one or more embodimentsof the disclosed subject matter.

FIG. 8 presents example healthcare worker information that facilitatesassigning the healthcare workers to healthcare tasks of an integratedhealthcare system in accordance with one or more embodiments of thedisclosed subject matter.

FIG. 9 illustrates an example, high-level flow diagram of acomputer-implemented process for coordinating and optimizing resourceutilization and delivery of healthcare services across an integratedhealthcare system using a machine learning framework, in accordance withone or more embodiments of the disclosed subject matter.

FIG. 10 illustrates another example, high-level flow diagram of acomputer-implemented process for coordinating and optimizing resourceutilization and delivery of healthcare services across an integratedhealthcare system using a machine learning framework, in accordance withone or more embodiments of the disclosed subject matter.

FIG. 11 illustrates another example, high-level flow diagram of acomputer-implemented process for coordinating and optimizing resourceutilization and delivery of healthcare services across an integratedhealthcare system using a machine learning framework, in accordance withone or more embodiments of the disclosed subject matter.

FIG. 12 illustrates another example, high-level flow diagram of acomputer-implemented process for coordinating and optimizing resourceutilization and delivery of healthcare services across an integratedhealthcare system using a machine learning framework, in accordance withone or more embodiments of the disclosed subject matter.

FIG. 13 illustrates a block diagram of an example, non-limitingoperating environment in which one or more embodiments described hereincan be facilitated.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Summary section or in theDetailed Description section.

The disclosed subject matter is directed to systems,computer-implemented methods, apparatus and/or computer program productsthat provide facilitate coordinating and optimizing resource utilizationand delivery of healthcare services across an integrated healthcaresystem using a machine learning framework. An integrated health caredelivery system is one in which all the providers whose services affecta patient work together in a coordinated fashion, sharing relevantmedical information, sharing aims or goals, sharing responsibility forpatient outcomes, and for resource use. For example, an integratedhealthcare system can include many different operating entities thatprovide a variety of different healthcare services to patients,including hospitals, specialized hospital units, specialized physicianclinics/offices, outpatient facilities, ambulatory services, nursinghome facilities, surgery centers, imaging/diagnostic providers, pharmacyproviders, traveling/in-home patient care, rehabilitation providers,telemedicine providers, and the like.

Various embodiments of the disclosed subject matter provide systems,methods and computer readable media that enable an integrated deliverysystem using a team-based approach to deliver healthcare services topatients across various patient care settings (e.g., inpatient,outpatient, home, doctor's office, telemedicine/virtual, etc.),balancing and coordinating all patient's needs, available operatingentity capabilities and resources in real-time and space. The disclosedtechniques are based on multi-dimensional care delivery model thatoptimizes care delivery and distribution of resources account forfactors in several dimensions, including an operating entity dimension,a patient dimension and a location and time dimension. The operatingentity dimension involves monitoring real-time demand for eachindividual operating entity and intelligently matching the necessarysystem wide resources to achieve optimal throughput. The components foreach discrete service or activity are catalogued whether it be humanlabor, supplies, equipment, or technology. As demand ebbs and flows, thedisclosed techniques can dynamically flex resources across multipleservice lines according to skill and attribution.

The second dimension of this model is the discrete patient that has aprescribed number of activities or services to be rendered. Taking intoaccount unique patient's acuity or need, list of discrete services oractivities to be completed, and any requires sequencing, the disclosedtechniques can determine how to schedule the patient for service tooptimize the time required to complete all activities. This sequencingcan take taking into account the scheduled steps progress, anticipateddate/time of service, the patient's physical location, required traveltime/distance, and the overall patient's available time window to havethese services rendered. A third dimension is space and time. In thisregard, the disclosed techniques further balancing the operating entitydimension with the patient dimension in real-time with the variables oftime and space. As a result, the entire system can be optimized todeliver improved operating entity performance while also improvingpatient movement through prescribed services and actions.

With this operating model in mind, various embodiments described hereinprovide a system is provided that can facilitate optimizing schedulingof different healthcare tasks and assigning resources to the differenthealthcare tasks in real-time in a manner that synchronizes andharmonizes patient needs and provider capabilities under the dynamicoperating conditions associated with the healthcare environment. Thehealthcare environment can include individual operating entities, aswell as a macro ecosystem that combines the individual operatingentities into a unified integrated healthcare system. For example, theoperating entities can include various types of healthcare facilitiesthat provide healthcare services, including (but not limited to),hospitals, clinics, ambulatory surgical centers, birth centers, bloodbanks, specialty clinics or medical offices, dialysis centers, hospicehomes, imaging and radiology centers, therapy centers, mental healthtreatment centers, nursing homes, orthopedic and other rehabilitationcenters, urgent care facilities, and the like.

In one or more embodiments, the system collects and combines real-timeand historical data from various integrated healthcare provider systemsand sources regarding patient needs and all aspects of operations of thedifferent healthcare providers that are available to provide healthcareservices to patients. In this regard, the system can access and retrieveor receive operating information from different operating entities inreal-time over a course of operating of the one or more operatingentities regarding what task needs to be done (e.g., clinical tasks andnon-clinical task for performance by a wide range of clinicians,healthcare workers and the like, when and where within the healthcaresystem at a current point in time and/or over a defined, upcoming periodof time. The system can further extract and receive up-to-dateinformation from the different operating entities regarding who or whomis available to perform the tasks, and who is the best person/persons toperform the tasks. The system can further evaluate the information usingvarious machine learning models and/or optimization models/algorithms todetermine how to schedule performance of the tasks with respect to timeand location and how to assign resources (e.g., workers and optionallynon-human resources) to the tasks in a manner that results in performingthe tasks in the most efficient and effective manner, using the rightresources at the right time for the right patient in the right place.For example, the system can determine how to optimize the operations ofindividual operating entities with respect to scheduling and managingperformance of all healthcare tasks at the individual operating entitiesin a manner that results in the most efficient and effective utilizationof available system resources while accounting for the collective andpersonal needs and preferences of all patients. Using similar machinelearning and optimization techniques, the system can further determinehow to optimize the operations of the different operating entities as awhole to achieve even greater efficiency in terms of resourceutilization while providing patients more personalized and timely accessto appropriate clinical care by coordinating and synchronizing patientand provider needs leveraging shared resources.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however, in various cases, that the one or more embodiments canbe practiced without these specific details.

Turning now to the drawings, FIG. 1 illustrates a block diagram of anexample, non-limiting system 100 for coordinating and optimizinghealthcare resource utilization and delivery of healthcare servicesacross an integrated healthcare system using a machine learningframework, in accordance with one or more embodiments of the disclosedsubject matter. Embodiments of systems described herein can include oneor more machine-executable components embodied within one or moremachines (e.g., embodied in one or more computer-readable storage mediaassociated with one or more machines). Such components, when executed bythe one or more machines (e.g., processors, computers, computingdevices, virtual machines, etc.) can cause the one or more machines toperform the operations described.

For example, in the embodiment shown, system 100 includes a healthcaredelivery optimization server device 108 and a plurality of healthcareinformation systems/sources 102. The healthcare delivery optimizationserver device 108 can include various computer executable components,including but not limited to, a task assessment module 110, a resourceassessment module 112 and a task scheduling and resource assignmentoptimization module 118. The healthcare delivery optimization serverdevice 108 can include or be operatively coupled to at least one memory124 and at least one processor 122. The at least one memory 124 canstore executable instructions (e.g., the task assessment module 110, theresources assessment module 114, and the task scheduling and resourceassignment optimization module 118) that when executed by the at leastone processor 122, facilitate performance of operations defined by theexecutable instructions. In some embodiments, the memory 124 can alsostore one or more of the various data sources and/or data structures ofsystem 100 (e.g., the healthcare information systems/sources 102, thedynamic operating data 104, the static/semi-static system data 106, theindexed task data 112, the resource availability data 116, and the taskscheduling and resource assignment information 126). In otherembodiments, one or more of the various data sources and/or datastructures of system 100 can be stored in other memory (e.g., at aremote device or system), that is accessible to the healthcare deliveryoptimization server device 108 (e.g., via one or more networks). Thehealthcare delivery optimization server device 108 can further include adevice bus 120 that communicatively couples the various components anddata sources of the healthcare delivery optimization server device 108(e.g., the task assessment module 110, the resources assessment module114, the task scheduling and resource assignment optimization module118, the processor 122, and the memory 124). Examples of said processor122 and memory 124, as well as other suitable computer orcomputing-based elements, can be found with reference to FIG. 12, andcan be used in connection with implementing one or more of the systemsor components shown and described in connection with FIG. 1 or otherfigures disclosed herein.

Although system 100 co-locates the task assessment module 110, theresource assessment module 114 and the task scheduling and resourcesassignment optimization module 118 at a same device (e.g., thehealthcare delivery optimization server device 108), it should beappreciated that these modules and/or one or more components of thesemodules (discussed in greater detail infra) can be provided in adistributed manner across various interconnected (via one or morenetworks) systems and devices (e.g., internal systems, the cloud, two ormore dedicated servers, etc.). In this regard, the healthcare deliveryoptimization server device 108 can be or correspond to a distributedcomputing system including a network of interconnected devices (e.g.,back-end servers, front-end servers, dedicated machines, virtualmachines, client devices, etc.), machine, databases, datastores and thelike.

In this regard, in some implementations, the healthcare deliveryoptimization server device 108, one or more of the various modules (andassociated components), and/or one or more of the various datasources/structures of system 100 (and other systems described herein)can be communicatively connected via one or more networks. Such networkscan include wired and wireless networks, including but not limited to, acellular network, a wide area network (WAD, e.g., the Internet) or alocal area network (LAN). For example, the healthcare deliveryoptimization server device 108 can communicate with the healthcareinformation sources/systems 102 using virtually any desired wired orwireless technology, including but not limited to: wireless fidelity(Wi-Fi), global system for mobile communications (GSM), universal mobiletelecommunications system (UMTS), worldwide interoperability formicrowave access (WiMAX), enhanced general packet radio service(enhanced GPRS), third generation partnership project (3GPP) long termevolution (LTE), third generation partnership project 2 (3GPP2) ultramobile broadband (UMB), high speed packet access (HSPA), Zigbee andother 802.XX wireless technologies and/or legacy telecommunicationtechnologies, BLUETOOTH®, Session Initiation Protocol (SIP), ZIGBEE®,RF4CE protocol, WirelessHART protocol, 6LoWPAN (IPv6 over Low powerWireless Area Networks), Z-Wave, an ANT, an ultra-wideband (UWB)standard protocol, and/or other proprietary and non-proprietarycommunication protocols. The computing device 108 can thus includehardware (e.g., a central processing unit (CPU), a transceiver, adecoder), software (e.g., a set of threads, a set of processes, softwarein execution) or a combination of hardware and software that facilitatescommunicating information between the healthcare delivery optimizationserver device 108 and externals systems, sources and devices.

The healthcare delivery optimization server device 108 can providevarious features and functionalities that facilitate optimizingutilization of healthcare resources and delivery of healthcare services.In one or more embodiments, the healthcare delivery optimization serverdevice 108 can facilitate optimizing scheduling of different healthcaretasks and assigning resources to the different healthcare tasks inreal-time in a manner that synchronizes and harmonizes patient needs andprovider capabilities under the dynamic operating conditions associatedwith the healthcare environment. The healthcare environment can includeindividual operating entities, as well as a macro ecosystem thatcombines the individual operating entities into a unified integratedhealthcare system. For example, the operating entities can includevarious types of healthcare facilities that provide healthcare services,including (but not limited to), hospitals, clinics, ambulatory surgicalcenters, birth centers, blood banks, specialty clinics or medicaloffices, dialysis centers, hospice homes, imaging and radiology centers,therapy centers, mental health treatment centers, nursing homes,orthopedic and other rehabilitation centers, urgent care facilities, andthe like. The operating entities can also include healthcare entitiesthat provide mobile or in-home care services patients (e.g., travelingnurses, emergency medical services). The operating entities can alsoinclude entities that provide virtual or telemedicine services, such asremote monitoring services, virtual radiology services, remote clinicalservices (e.g., consolations, diagnostic appointments, check-ups,question and answer sessions, mental health sessions, etc.) deliveredvia phone and/or video conferencing, and the like. In someimplementations, the operating entities can also include informationtechnology systems that provide and/or execute medical software productsused in association with clinical workflows.

In the embodiment shown, the healthcare information systems/sources 102can include a variety of electronic data sources and/or systemsassociated with one or more operating entities of a healthcare system(e.g., an integrated healthcare system) that employs the features andfunctionalities of the healthcare delivery optimization server device108. The healthcare information sources/systems 102 can provide avariety of dynamic operating data 104 and static/semi-static system data106 associated with the respective operating entities that can be usedby the healthcare delivery optimization server device 108 as input forvarious machine learning and/or statistical models configured togenerate inferences/determinations in real-time regarding how tooptimize utilization of available healthcare resources and delivery ofhealthcare services by the respective operating entities over a courseof operation of the respective operating entities.

In this regard, the dynamic operating data 104 can include informationregarding operating conditions of a healthcare operating entity (e.g., ahospital, a surgery centers, a nursing home, etc.) that constantly orregularly change over relatively short timeframes during a course ofoperation of a healthcare operating entity (e.g., by the second, by theminute, by the hour, by the day, etc.). For example, the dynamicoperating data 104 can include information provided by patientscheduling systems, patient monitoring systems, patient trackingsystems, operational data logging systems, workflow tracking systems,resource tracking/monitoring systems, and the like. In one or moreimplementations, the dynamic operating data 104 can include varioustypes of information for each individual operating entity thatidentifies or indicates the various healthcare tasks that are needed forperformance by/at the operating entity at a current point in time/orover a defined, upcoming timeframe (e.g., the current workday, the next24 hours, the next week, the next month, etc.) to account for known andoptionally forecasted patient needs at the current point in time andover. The dynamic operating data 104 can also include informationregarding the state, status/condition, availability, movement, location,and other dynamic parameters associated with the healthcare resourcesthat are needed to perform the healthcare tasks and/or the patientassociated with the healthcare task.

In this regard, the term healthcare task as used herein can includeessentially any defined (predefined or defined by the task assessmentmodule 110) task involved in a course of patient care that is providedby and/or controlled by one or more individuals that are associated withthe operating entity or entities involved in the course of patient care.The one or more individuals can include employees/staff, volunteers,independent contractors, patients, and/or patient aids (e.g., a friendor family member of the patient, a personal assistant/aid of thepatient, etc.). For example, the healthcare tasks can include clinicaltasks, clerical tasks, administrative tasks, environmental servicestasks and the like, performed by and/or facilitated by clinicians,physicians, nurses, pharmacists, social workers, therapists, emergencyservices clinicians/technicians, community health workers,transportation workers, care coordinators, law enforcement,complimentary/alternative medicine practitioners, behavioral healthworkers, environmental services workers, food service workers, and thelike. In some implementations, the healthcare tasks can includenon-clinical tasks, such as tasks involving transporting patients and/orsupplies, EVS tasks, task involving equipment/technology repair andmaintenance, administrative tasks (e.g., verifying insurancequalifications, obtaining authorization for procedures, etc.), and thelike. Although various embodiments are described with the assumptionthat the one or more individuals include humans, in someimplementations, the one or more individuals can include machines thatare configured to perform certain healthcare tasks autonomously and/orat the control of a human (e.g., intelligent machines, robots,self-driving vehicles, etc.). The term healthcare worker is used hereinto refer to any individual or machine associated with an operatingentity that can perform a healthcare task.

The static/semi-static system data 106 can include informationassociated with the respective operating entities, employees of theoperating entities, and patients that does not change over time and/ormay change at a relatively infrequent rate compared to the dynamicoperating data 106. For example, the static/semi-static system data caninclude information stored one or more databases regarding definedsystem protocols, regulations, operating requirements, employeeadministrative information, patient electronic health records (EHRs),patient imaging studies, patient laboratory studies, clinicalorders/notes, patient preference information, employee performancerecords and the like. Additional details regarding the healthcareinformation systems/sources 102 and the types of dynamic operating data104 and static/semi-static system data 106 that can be used by thehealthcare delivery optimization server device 108 are described ingreater detail infra with reference to FIG. 2.

In various embodiments, the healthcare delivery optimization serverdevice 108 can access and retrieve or receive dynamic operating data 104and static/semi-static system data 104 for one or more operatingentities (e.g., operating entities of an integrated healthcare system)in real-time over a course of operating of the one or more operatingentities. In some implementations, the healthcare delivery optimizationserver device 108 can also access, retrieve and/or collect historicalsets of the dynamic operating data 104 and correspondingstatic/semi-static system data 106 (e.g., for developing, trainingand/or tuning one or more machine learning optimization models employ bythe healthcare delivery optimization server device 108, as discussed ingreater detail infra). The healthcare delivery optimization serverdevice 108 can further evaluate the dynamic operating data 104 and thestatic/semi-static system data 106 using various machine learning modelsand/or optimization models/algorithms to determine how to optimize theoperations of individual operating entities with respect to schedulingand managing performance of all healthcare tasks at the individualoperating entities in a manner that results in the most efficient andeffective utilization of available system resources while accounting forthe collective and personal needs and preferences of all patients. Usingsimilar machine learning and optimization techniques, the healthcaredelivery optimization server device 108 can further determine how tooptimize the operations of the different operating entities as a wholeto achieve even greater efficiency in terms of resource utilizationwhile providing patients more personalized and timely access toappropriate clinical care by coordinating and synchronizing patient andprovider needs leveraging shared resources.

The various features and functionalities of the healthcare deliveryoptimization server device 108 can be dived into three components,including a task assessment component performed by the task assessmentmodule 110, a resources assessment component performed by the resourceassessment module 114, and a task scheduling and resource assignmentoptimization component performed by the task scheduling and resourceassignment optimization module 118.

The task assessment component involves the extraction, evaluation andindexing of information from the healthcare information systems/sources102 regarding healthcare tasks performed, being performed, scheduled forperformance and/or anticipated (forecasted) for performance byrespective operating entities included in the integrated healthcaresystem. In this regard, the task assessment module 110 can extractinformation from various information systems/sources (e.g., thehealthcare information systems/sources 102) associated with therespective operating entities regarding all (or grouped subset of) thevarious healthcare tasks that need to be performed by the respectiveoperating entities from a current point in time and into a defined,upcoming timeframe (e.g., the workday, the next hour, the next 24 hours,the next week, the next month etc.) to satisfy all (or a grouped subset)current and future (e.g., known or forecasted) patient needs. The taskassessment module 110 can further evaluate the task information toidentify discrete healthcare tasks associated with each patientrepresented in the extracted data. For example, the task assessmentmodule 110 can evaluate information for a patient identifying orindicating scheduled procedures, appointments and checkups, identifyingclinical orders, defining a prescribed care plan, defining a medicationregimen, defining a feeding regimen, tracking patient status,identifying patient transfer needs (including current and destinationlocation of the patient), providing real-time feedback requesting orindicating immediate or future medical care (e.g., provided by thepatient, gathered indirectly via a patient monitoring system), and thelike. Based on evaluating this information, the task assessment module110 can identify discrete healthcare tasks for performance by one ormore healthcare workers to satisfy the patient's needs and/or that arerequired for provision to the patient based on defined protocols,regulations, requirements, service level agreements (SLA)s, etc., of theoperating entity or entities responsible for the patient.

The task assessment module 110 can further determine and associateattribute information with each discrete task regarding constraints,conditions and/or requirements that can influence if the task isperformed (e.g., based on authorization restrictions, necessity etc.),when the task is performed, where the task is performed, how the task isperformed (e.g., in person, using telemedicine), who or whom performsthe task (e.g., a person or person), and/or what additional (non-human)resources are used (e.g., supplies, instruments, equipment, technology,etc.). For example, the task assessment module 110 can determine andassociate attribute information with each task that identifies a definedtype or classification of the task (e.g., determined based on predefinedtask classification/type coding system), a time of origination of thetask, a time or time frame for completing the task, an expected durationof the task, a location associated with the task, a priority levelassociated with the task, an and the like. The task assessment module110 can also determine and associate information with a task thatindicates patient preference regarding when, where, by whom and/or howthe task is performed. In some embodiments, the task assessment module110 can also evaluate tasks associated with a single patient ordifferent patient to identify and associate dependency information withthe respective tasks regarding dependencies between two or more tasks,such as dependencies that restrict or control order of performance ofthe tasks, and/or that restrict or control performance of the tasks by asame healthcare worker or healthcare team (e.g., including two or morehealthcare workers). The task assessment module 110 can also determineand associate resource attributes with the healthcare tasks thatidentify or indicate requirements for resources to be used for thetasks, including requirements for healthcare workers authorized toperform the tasks (e.g., based on credentials, job description,performance levels, fatigue levels, etc.), as well as requirements fornon-human resources, such as required medications, medical supplies,devices, equipment, technology and the like for use in association withperforming the respective tasks.

The task assessment module 110 can further generate indexed task data112 that comprises one or more data structures that organizes andindexes task information regarding all the discrete tasks, theirassociated attributes and/or interdependencies. The task assessmentmodule 110 can further regularly and/or continuously update the indexedtask data 112 in real-time over the course of operation the integratedhealthcare system to reflect changes to the integrated healthcaresystem. For example, the task assessment module 110 can regularly and/orcontinuously update the indexed task data to reflect progress ofperformance and completion of the tasks, to reflect newly added tasks,to reflect changes in operating conditions that effect one or moreattributes of the tasks, and the like. In this regard, the indexed tasksdata 112 can be or correspond to a dynamic monitor of what healthcaretasks should/could be done to take care of all current (and optionallyforecasted) patient needs from a current point in time and into thefuture (to a defined time point in the future), while also providingrelevant information that can influence when, where, how, and by whomthe healthcare tasks are performed to optimize utilization of availablesystem resources while also accounting for patient needs andpreferences.

Additional details regarding the features and functionalities of thetask assessment module 110 are discussed in greater detail infra withreference to FIG. 3.

The resource assessment component involves the extraction and evaluationof information from the healthcare information systems/sources 102regarding the availability of resources of the one or more operatingentities to perform the currently pending and upcoming healthcare tasks.In various embodiments, the resources of interest are the individuals(e.g., manual resources) that can perform the healthcare tasks (e.g.,the doctors, nurses, therapists, aids, technicians, transportationworkers, food service workers, ambulatory technicians, paramedics,etc.), collectively referred to herein as healthcare workers. In thisregard, the resource assessment module 114 can be configured to extractand evaluate the dynamic operating data 104 regarding the activity ofthe various healthcare workers in real-time in view of any schedulingconstraints for the healthcare workers to determine resourceavailability data 116 regarding availability of the respectivehealthcare workers to perform currently pending tasks and/or upcomingtasks (e.g., known, scheduled, and/or forecasted). The resourceassessment module 114 can further determine and generate resourceavailability data 116 that identifies the respective healthcare workers(e.g., by name or another resource identification number) and providesinformation regarding the availability of the respective workers at acurrent point in time and/or over a defined, upcoming timeframe (e.g.,the workday, the workweek, the next hour, the next 24 hours, the nextmonth, etc.).

For example, the resource availability data 116 can include but is notlimited to, information that identifies a current availability status ofthe healthcare workers (e.g., available, unavailable), that identifiesor indicates durations of time until the respective workers will becomeavailable to perform a healthcare task (e.g., available now, availablebetween hours 1400 and 1500, available in 30 minutes), amount of timethe respective workers have available to perform certain tasks (e.g.,based on scheduling constraints, shift end time, etc.), and locations ofthe respective healthcare workers. In some embodiments, the resourceavailability data 116 can include information that identifies known orexpected timeslots over a defined upcoming timeframe (e.g., the next 24hours, the next week, the next month, etc.) in which one or morehealthcare workers have available to perform healthcare tasks, includingdurations of the timeslots and in some implementations, locationsassociated with the time slots (e.g., a specific office location). Insome embodiments, the resource availability data 116 can furtheridentify available time that the resource assessment module 114classifies as idle time. As discussed in greater detail infra, idle timecan include time in which a healthcare worker is between tasks,traveling (e.g., driving, walking, riding as a passenger, etc.), eatinglunch, waiting for laboratory results, or the like, that can be utilizedto perform supplementary healthcare tasks, such as telemedicine tasks.In some embodiments, the resource availability data 116 can also includeinformation regarding a physiological state of the healthcare workers(e.g., fatigue level, stress level, intoxication level, etc.) that canbe used to facilitate determining if a particular healthcare worker isin a suitable state to perform certain healthcare tasks of therespective workers, and the like.

For example, in one or more embodiments, the resource assessment module114 can receive dynamic operating data 104 that tracks or facilitatestracking performance of healthcare tasks by the respective healthcareworkers in real-time, such as information regarding if a healthcareworker is currently performing a healthcare task, what tasks respectivehealthcare workers are currently performing, scheduled tasks therespective workers are scheduled to perform (including where and when),timing of imitation of tasks and status of progression through the tasks(e.g., to facilitate determining expected time of completion of thetasks), and the like. In some implementations, the resource assessmentmodule 110 can also forecast estimated timing of completion of task thatare in-progress (e.g., using one or more machine learning techniques).The resource assessment module 114 can further evaluate schedulinginformation to determine information regarding amount of time certainworkers have available between scheduled task, taking into accountlocations of the scheduled task, mode of travel between differentlocations, and expected amounts of time the scheduled task take tocomplete (e.g., using one or more machine learning techniques). Theresource assessment module 114 can also receive and track informationregarding location of the respective healthcare workers, movement of thehealthcare workers, mobility state in association with traveling fromone location to another (e.g., walking, driving, riding as a passenger,etc.). In some embodiments, the resource assessment module 114 canemploy one or more machine learning techniques to learn and forecastinformation regarding availability of certain healthcare workers basedon analysis of historical dynamic operating data 104 associated with thespecific healthcare workers or similar healthcare workers (e.g., withsimilar job titles, skill levels, location etc.) under various operatingconditions/contexts of the healthcare environment. In someimplementations, these machine learning techniques can involveforecasting upcoming demand and expected time in which the healthcareworker will have to perform a healthcare task given the forecasteddemand.

Although the embodiments, described above are directed to evaluating anddetermining availability of healthcare workers (e.g., humans), theresource assessment module 114 can also evaluate relevant dynamicoperating data 104 to determine information regarding the availabilityof other system resources. For example, the other system resources caninclude supplies, instruments, equipment, machines, technology, and thelike that are needed to perform and/or facilitate performance of thehealthcare tasks. Thus, in some embodiments, the resources availabilitydata 116 can also include information regarding availability of otherresources, such as current availability status of the resources (e.g.,whether they are in-use, clean, dirty, in repair, offline, overloaded,power levels, etc.), expected availability status of the resources(e.g., when they will be available for use), locations of the resources,and the like.

Additional details regarding the features and functionalities of theresource assessment module 114 are discussed in greater detail infrawith reference to FIG. 5.

Turning now to the third component, using parameters provided by theindexed task data 112, the resource availability data 116, the dynamicoperating data 104 and the static/semi-static system data 106 as input,the task scheduling and resource assignment optimization module 118 canemploy one or more machine learning and/or optimizationalgorithms/models to determine how to schedule performance of thehealthcare tasks to optimize utilization of the available resources inview of patient preference and need. In this regard, the task schedulingand resources assignment optimization module 118 can intelligentlydetermine if a task should performed (e.g., based on authorizationrestrictions, necessity etc.), when the task should performed, where thetask should performed, how the task should be performed (e.g., inperson, using telemedicine), who or whom should perform the task (e.g.,a person or person), and/or what additional (non-human) resources shouldbe used (e.g., supplies, instruments, equipment, technology, etc.),based one or more optimization criteria. For example, the optimizationcriteria can include (but is not limited to), facilitating optimalpatient flow, minimizing delay between performance of tasks, meetingfixed constraints (e.g., regarding timing and order, location,quality/standard of care, etc.), meeting patient preferences/needs,maximizing utilization of resources, minimizing costs, and/or maximizingrevenue.

In association with determining if, when, where, and by whom a pendinghealthcare task should be performed, the task scheduling and resourceassignment optimization module 118 can evaluate the collective state ofthe entire healthcare environment to determine an optimal scheduling ofall (or a grouped subset) of known tasks to be performed (e.g., within adefined, upcoming timeframe). In this regard, the task scheduling andresource assignment module 118 can determine an optimal scheduling(time/location) and resource assignment (specific healthcare workers)arrangement that balances and coordinate constraints, requirements andconditions associated with all tasks with respect to timing, location,and resources to be used for the tasks (e.g., provided by and/ordetermined by the indexed task data 112), patient needs and preferences(e.g., provided in the static/semi-static system data 106), availabilityof resources to perform the tasks (e.g., provided by the resourceavailability data 116), and various attributes associated with therespective resources.

For example, with respect to assigning workers to healthcare tasks, inaddition to availability, the task scheduling and resource assignmentoptimization module 118 can match the “best” worker to the task thatmaximizes utilization of the available workers based on qualification toperform the tasks, skill level, proficiency level, compensationschedule, worker preference, and the like. Further, as discussed ingreater detail infra with reference to FIG. 7, the task scheduling andresource assignment optimization module 118 can employ one or moreoptimization models that maximize utilization of workers by repurposinguses of the workers in appropriate contexts beyond their primary roleand sharing workers across different operating entities based on contextand need. With these embodiments, the integrated healthcare system (orindividual operating entities) can define all possible types ofhealthcare tasks each worker can perform based on their qualifications,capabilities, performance level and the like. The healthcare tasks caninclude healthcare tasks that are traditionally associated with the jobtitle/description, as well as those that are outside of theirtraditional or primary capacity. For example, the system can identify aset of primary healthcare tasks of a particular surgeon included in theintegrated healthcare system, such as the specialized surgery proceduresthe surgeon is capable of performing. The system can further provide alist of various alternative healthcare tasks the surgeon can perform,including non-surgical tasks, various general clinical taskstraditionally performed by nurses or less specialized clinicians, aswell as non-clinical tasks, such as administrative tasks, EVS tasks, andthe like. According to these embodiments, the task scheduling andresource assignment optimization module 118 can assign healthcareworkers to various types of tasks based on context and need, includingtasks that are not their primary role when appropriate to maximizeutilization of all workers in every capacity. In some embodiments, thetask scheduling and resource assignment optimization module 118 can alsofactor in expected/forecasted need of certain workers, balancing costsassociated with assigning them to a certain task now or waiting toassign them to a more demanding or appropriate task expected to arise inthe near future.

In this regard, the task scheduling and resource assignment optimizationmodule 118 can evaluate information for an entire operating environment(e.g., of a single entity or an integrated healthcare system) regardingwhat needs to be done and when, who is available to do it, and who isthe best person/persons to do it, based on a variety of complex anddynamic variables, to determine how to schedule performance of the taskswith respect to time and location and how to assign resources (e.g.,workers) to the tasks to ensure the tasks are performed in the mostefficient and effective (e.g., in terms of achieve quality of care, interms of minimizing costs, in terms of maximizing revenue, and the like)manner across all possible options, using the right resources at theright time for the right patient in the right place. From theperspective of the operating entity, or entities in embodiments in whichthe disclosed techniques are applied to an integrated healthcare system,the optimal scheduling and resource assignment scheme for all (or agrouped subset) of known tasks to be performed (e.g., within a defined,upcoming timeframe) can be selected to balance one or more of thefollowing goals: minimize delays between performance of the healthcaretasks, ensure all healthcare tasks are delivered in accordance withdefined quality and regulatory requirements, maximize utilization ofavailable resources, minimize losses, maximize revenue, and meet patientpreferences with respect to when, where and who performs healthcaretasks. In embodiments in which the disclosed techniques are applied toan integrated healthcare system involving a plurality of differentoperating entities, the optimal task scheduling and resource assignmentscheme can further balance the goals across the entire system, furtherconsidering the many additional constraints associated with coordinatingand synchronizing performance of healthcare tasks with interdependencieswith respect to order of performance, timing of performance andresources used. The optimal task scheduling and resource assignmentscheme for an integrated healthcare system can also consider geographicfactors/constraints involving operating entities at various disparatelocations, which can further influence where services are scheduled, andtiming of scheduling based on travel time, traffic, and the like. Insome embodiments, the tasks scheduling and resource assignmentoptimization module 118 can generate several different scheduling andresource assignment schemes for performing healthcare tasks usingdifferent optimization models that are targeted to different goals. Forexample, one optimization model can be configured to determine a taskscheduling and resource assignment scheme that focuses more heavily onminimizing delays between performance of healthcare tasks, while anothermodel can be configured to determine an alternative scheme that focusesmore heavily on meeting patient preferences. In this regard, theoptimization models can be tailored based on the needs and preferencesof the specific operating entity and/or integrated healthcare systemthat employs system 100 to facilitate optimizing their operations.

The task scheduling and resource assignment optimization modules 118 canfurther generate task scheduling and resource assignment information 126that can be regularly updated in real-time regarding the determinedoptimal scheduling/resource assignment scheme or schemes for anoperating entity and/or integrated healthcare system. For example, taskscheduling and resource assignment information 126 can identify knownhealthcare task to be performed at a current point in time and/or over adefined upcoming timeframe, and include information identifying a timingfor performance of the respective tasks, a location for performance ofthe respective tasks, the specific healthcare worker or group ofhealthcare workers assigned to the task, and in some implementations,specific instruments, supplies, equipment, etc., assigned to therespective tasks. The healthcare tasks can include all known healthcaretasks for integrated healthcare system or subsets of the tasks groupedby a defined grouping criterion, such as by operating entity, bylocation, by patient, by healthcare worker, by timeframe, etc. The taskscheduling and resource assignment information 126 can further beprovided to the task management administrators and/or the healthcareworkers directly to facilitate performing the healthcare tasks inaccordance with the prescribed optimal scheduling and resourceassignment scheme. The task scheduling and resource assignmentinformation 126 can also be provided to individual patients to provide areal-time schedule of activities for each patient with anticipateddate/time of event and coordinates the sequencing of various activitiesand services to be rendered (and updated in real-time).

Additional details regarding the features and functionalities of thetask scheduling and resource assignment optimization module 118 arediscussed in greater detail infra with reference to FIG. 7.

FIG. 2 illustrates example healthcare information systems/sources 102that can provide information that facilitates coordinating andoptimizing resource utilization and delivery of healthcare servicesacross an integrated healthcare system using a machine learningframework, in accordance with one or more embodiments of the disclosedsubject matter. The various healthcare information systems/sources shownin FIG. 2 can include databases and systems associated with a singleoperating entity or a plurality of different operating entities of anintegrated healthcare system. The various databases and systems shown inFIG. 2 are merely exemplary and are intended to limit the scope of thevarious possible data sources and systems that can provide the dynamicoperating data 104 and the static/semi-static system data 106.Repetitive description of like elements employed in respectiveembodiments are omitted for sake of brevity.

In the embodiment shown, the healthcare information systems/sources 102include one or more databases that provide static/semi-static systemdata 106 for an operating entity or group of operating entities,including task definitions/requirement data 202, worker information 204,system geospatial data 206, finance data 210 and patient information252. The task definitions/requirements data 202 can include predefinedinformation that identifies discrete healthcare tasks that are performedby one or more healthcare workers of a healthcare system. The healthcaretasks can include various types of tasks that are performed by a singleoperating entity and/or that are performed in an integrated healthcaresystem, including but not limited to, clinical tasks, administrativetasks, EVS tasks, transportation services tasks, food services tasks,technical tasks, ambulatory tasks, and the like. In various embodiments,the task definitions/requirements data 202 can identify defined tasksusing a unified or standardized classification system that assigns eachdiscrete task with a unique task identifier that identifies the task andidentifies or indicates the requirements of the tasks. For example, insome implementations, known medical procedures can be identified asdiscrete tasks by standardized procedure codes for the respectivemedical procedures. Some medical procedures can involve a plurality ofassociated tasks, such as task that involve preparing the patient, tasksthat involves administering medication, task that involve performingsurgery, task that involve assisting the surgeon, tasks that involveperforming an assessment, tasks that involve operating medicalinstruments, tasks that involve waste removal, task that involvefacilitating patient recovery, and the like. In this regard, a singleprocedure can be associated with a plurality of discrete tasks, whereineach task can further be defined with a unique that identifies thespecific task and indicates requirements of the task.

In some implementations, the unique identifier used for each task canidentify or indicate a type of the task such as clinical vs.non-clinical, if clinical a type of the clinical task (e.g., a medicalprocedure, a medical assessment, an administering of medication, aprescribing of medication), if non clinical, a type of the non-clinicaltask (e.g., EVS, transportation services, equipment repair, foodservices, etc.). The task definitions/requirements data 202 can alsoinclude information that defines what the task involves, and/or anyfixed constraints associated with the respective tasks regarding rulesand/or requirements associated with the respective tasks. For example,the definitions/requirements data 202 can include information specifyingqualifications and/or credentials of the healthcare workers or workersthat are authorized to perform the task. For instance, thequalifications/credentials can list required certifications, levels ofexperience, performance rating levels and the like. In someimplementations, a task can be performed by a wide range of healthcareworkers with different types of qualifications, such as a licensedparamedic, a midwife, a nurse, a nurse practitioner, a physician'sassistant, a general physician, and a specialized physician. With theseimplementations, the qualifications/credentials information can includesystem preference information that ranks the different types ofcredentials/qualifications accepted based on preference for performingthe task, wherein workers with a certain credentials are considered morepreferred than workers with other credentials (e.g., this task is mostpreferably performed by a nurse, second most preferably performed by anurse practitioner, and third most preferably performed by a physician).The healthcare worker requirements information can also identify anumber of healthcare workers required and/or preferred for performingcertain tasks.

The rules/requirements associated with a task can also includeinformation identifying or indicating a relative priority level of atask (e.g., in accordance with a defined priority level coding/rankingscheme), as well as information identifying or indicating anyperformance dependency constraints with other tasks. For example,performance dependency constraints can identify or indicate an order forperforming a task relative to another related task. Performancedependency constraints can also identify or indicate task groupingsincluding tasks that must be performed together and/or by a samehealthcare worker or healthcare workers. Other examplerules/requirements information that can be associated with defined taskscan include rules/requirements regarding non-manual supplies to be usedin association with performing the tasks, such as required medicalsupplies, instruments, equipment, etc.

The healthcare worker information 204 can include known informationabout the different healthcare workers employed by and/or affiliatedwith (e.g., as volunteers, independent contractors, as personal patientaids/friends/family, etc.) an operating entity or group of operatingentities of an integrated healthcare system. In this regard, thehealthcare worker information 204 can include information identifyingthe respective healthcare workers (e.g., by name or another uniqueidentifier) and their job title or role. The healthcare workerinformation 204 can further include (but is not limited to), informationidentifying the primary operating entities or entities where thehealthcare worker works, the location of the operating entity orentities (including a geographic area where the worker travels forwork), and their employment history with the operating entity orentities. The healthcare worker information 204 can also include (but isnot limited to), compensation information identifying the compensationschedule/salary of the workers, their demographic information (e.g.,gender, age, ethnicity, etc.), home address/location, their contactinformation (e.g., phone number, email address, social medialconnections, etc.) and the like.

In various embodiments, the healthcare worker information 204 canfurther include task capability information that identifies (e.g., bytask identifier) or indicates discrete healthcare tasks (or groups oftasks) the healthcare worker is capable of and/or qualified to perform.With these embodiments, various healthcare workers can be capable ofperforming and/or qualified to perform a variety of different healthcaretasks in the healthcare system. These different healthcare tasks caninclude those that traditionally fall under their job title and/or thatare their primary tasks in the system given their defined role in thesystem, as well as various alternative or secondary healthcare tasksthat may not be traditionally performed by their job title/description.In this regard, the healthcare worker information 204 can provide a listof all possible healthcare tasks that are performed (or that may arise)in the healthcare system that the healthcare worker is capable andqualified to perform, enabling the system the ability to utilize eachworker in the best capacity depending on the contextual needs of thesystem and the contextual availability of workers capable of performingthe types of tasks that are needed for performance at any given time.

In some implementations of these embodiments, the healthcare workerinformation 204 can further include preference information for therespective tasks each worker is capable/qualified to performed regardinga preference of performance of the respective tasks. For example, thepreference information can include system defined preference informationfor each task/worker combination that reflects the healthcare system oroperating entity's preference for utilization the healthcare worker forthe specific task relative to other tasks the healthcare worker iscapable/qualified to perform. For instance, assume a surgeon is capableof performing a highly complex procedure as well as various generalclinical tasks. With this example, the system can prefer the surgeonperform the highly complex procedure over the general clinical tasks, asthis would in most scenarios be the most useful application of thesurgeon's skills. Thus, the system can associate a higher preferencerating with the complex procedure relative to the general clinicaltasks. The preference information can also include worker preferencerating information that provides a worker defined preference rating forperforming different task reflective of the workers' personalpreferences for performing certain tasks over others that the worker iscapable/qualified to perform.

The task capability information can further include informationassociated with the respective tasks that a worker is capable/qualifiedto perform regarding historically tracked and/or scored performancemetrics for the respective tasks. For example, the performance metricscan include a general performance rating provided by the healthcaresystem that reflects the performance quality (e.g., measured by systemreview, employee feedback, patient feedback, etc.), efficiency, andproficiency of the healthcare worker in association with performance ofeach task. The performance metrics can also include informationregarding the number and/or frequency of performance of the respectivetasks. The performance metrics can also include information regardinghistorical error/complication rate. The task capability information canalso include information regarding compensation schemes for compensatingthe healthcare worker for performing the different tasks. For example,in some implementations, a healthcare worker can be paid the same rateregardless of the specific task that the worker performs. In otherembodiments, the healthcare worker can be paid different rates dependingon the type of task and/or the specific task.

The system geospatial data 206 can include mapping information regardingthe relative physical locations of fixed structures and objects of ahealthcare environment. For example, the geospatial data 206 can includemapping information for a single medical facility, including informationidentifying the physical address of the medical facility, as well asmapping information identifying the physical layout of the facility,including the layout and locations of different floors, medical units,rooms, hallways, doors, etc. In implementations in which the healthcareenvironment includes an integrated healthcare system with a plurality ofdifferent operating entities provided at various different physicallocations (e.g., within a same city, state, region, country etc.), thesystem geospatial data 206 can also include mapping information for eachindividual operating entity.

The regulatory information 208 can include defined rules or regulationsthat provide guidelines regarding how to perform specific task and/orprocedures circumstances. These rules or regulations are generallyreferred to as standard operating procedures (SOPs). For example,emergency room physicians have SOPs for patients who are brought in anunconscious state; nurses in an operating theater have SOPs for theforceps and swabs that they hand over to the operating surgeons; andlaboratory technicians have SOPs for handling, testing, and subsequentlydiscarding body fluids obtained from patients. Medical procedures canalso be associated with SOPs that provide guidelines that define how toperform the procedure (e.g., steps to perform and how to perform them),how to respond to different patient conditions in association withperformance of the procedure, how to respond to complications thatarise, and other type of events that may arise over the course of theprocedure, and the like. Some healthcare organizations can alsoestablish or adopt SOPs for medical conditions that can define standardmedical practices for treating patient's having the medical conditionand the respective medical conditions. Some healthcare organizations canalso have SOPs regarding providing healthcare to patients having two ormore medical conditions (e.g., referred to as comorbidity). In thisregard, the regulatory information 208 can include information thatidentifies and/or defines one or more standardized or defined protocolsfor following in association with performance of a procedure, treating apatient with a condition, and/or responding to a clinical scenario. Forexample, with respect to a procedure, the workflow information canidentify procedural steps or processes to be performed and when, timingof the steps, information regarding how each step/process should beperformed, and the like

The finance data 210 can include any type of financial informationpertaining to costs associated healthcare task and services. Forexample, the finance data 210 can include costs associated withdifferent procedures and utilization of different resources, includinghuman resources as well as medical instruments and supplies. In someimplementations, the finance data 212 can include historical costs, suchas historical costs associated with past procedures, courses of care andthe like. For example, with respect to past procedures, the finance data210 can identify total costs (e.g., to the healthcare organization) ofeach procedure performed, as well as line item costs associated withindividual components of the procedures, including supplies/equipmentcosts, personnel costs, room/space costs, individual process orprocedural steps, and the like. In some implementations, finance data210 can also include information regarding reimbursement for respectiveprocedures, including total reimbursement and reimbursement forprocedural components. In some implementations, the finance data 210 canalso include cost information attributed to LOS, procedurecomplications, and procedural and/or clinical errors (e.g., includingcost attributed to litigation associated with the error).

The patient information 252 can include patient information regardingcurrent patients of one or more healthcare systems (e.g., patients thathave entered the healthcare system via at least one entry point) andtheir medical needs. For example, the patient information 252 caninclude care plan information 240 that describes or defines care plansfor the patients (if available). For example, the care plan can includeinformation a list or timeline of the various prescribed clinicaltreatment to for the patient in association with a course of patientcare. In some implementations, the care plan information can also beassociated with information identifying patient rest and recoveryperiods/times, such as amounts of time and/or periods of time duringwhich the patient is required or preferred to rest (e.g., betweenprocedures or appointments and the like). In some implementations, thecare plan information can be automatically generated and provided by anartificial intelligence (AI) system configured to evaluate a patient'scondition, diagnosis, needs and medical history and generate a care planaccordingly. In some implementations, the AI system can also evaluateinformation regarding the patient's insurance plan/carrier and/or formof payment in association with determining the type of services that areavailable to the patient for including in the patient's care plan (e.g.,only those services that are approved or anticipated for approval by thepatient's insurance provider). The patient information 252 can alsoinclude clinical order data 242 providing clinical order prescribed fora patient. The patient information can also include medical historyinformation for current and past patients, such as that provided in theEHR data 244. For example, the patient medical history information caninclude internal information for patients associated with a singlehealthcare organization as well as information aggregated for patientsacross various disparate healthcare organizations/vendors (e.g.,internal and third-party organizations/vendors) via a healthcareinformation exchange system (HIE). The patient information 252 can alsoinclude patient preferences data 248 that describes any patientpreferences regarding where, when, how and by whom the would prefer toreceive various medical services (e.g., preferred locations, preferredscheduling times, preferred rest times/amounts, preferredcharacteristics of the healthcare workers, etc.). The patientinformation 252 can also include insurance information 250 for thepatients that can control if a patient can receive certain services,where they can receive them, costs of services, and the like.

The healthcare information systems/sources 102 include also one or moresystems that provide dynamic operating data 104 for an operating entityor group of operating entities (e.g., of an integrated healthcaresystem), including one or more task reporting systems 212, one or moretask scheduling systems 216, one or more task forecasting systems 220,one or more task performance tracking systems 224, one or more workermonitoring systems 228, one or more patient monitoring systems 232, andone or more operating conditions tracking systems 236.

The one or more task reporting systems 212 can include various systemsthat facilitate reporting known healthcare tasks that need to becompleted at a healthcare system. For example, the one or more taskreporting systems 212 can include data entry systems associated varioushealthcare operating entities, units, departments, etc., that receiveuser input reporting new tasks that have be generated in the system. Forinstance, the new tasks can be reported via text, via audio feedback, byelectronic clinical ordering systems, and the like. These task reportingsystems 212 can provide newly reported task data 214 regarding thetasks. For example, the newly reported task data 214 can identify thetask and include any user/system provided attributes associated with thetasks (e.g., regarding the patient associated with the task, timingconstraints associated with the task, location constraints associatedwith the task, etc.). In some implementations, the task reportingsystems 212 can include patient request systems that receive patientinitiated request for medical services which can be treated as newlyreported tasks. In other implementations, the task reporting systems 212can include one or more automated systems configured to automaticallydetermine and generate new tasks to be performed based on new datapoints included in the dynamic operating data 104. For example, the taskreporting systems can include an AI system that determines new tasks tobe performed based on information regarding results/outputs ofpreviously completed tasks, monitored changes in patientconditions/status, monitored changes in healthcare worker taskperformance, and the like. For instance, the AI system can evaluatenewly received laboratory data for a patient to determine a new taskthat needs to be performed in response to the specific values reflectedin the laboratory data.

The one or more task scheduling systems 216 can include electronicscheduling systems for one or more operating entities that are used toschedule task, such as patient appointments, patient procedures and thelike. In this regard, the task scheduling systems 216 an provide taskscheduling data 218 regarding tasks scheduled for performance atspecific times, places and/or locations and with specific clinicians.The task scheduling data 216 can also provide scheduling information forhealthcare workers regarding their scheduled tasks and appointments andscheduling information for patients regarding their scheduledappointments.

The task forecasting systems 220 can include various machine learningsystems configured to generate forecasted data 222 for a healthcaresystem regarding forecasted tasks expected to arise over a defined,upcoming timeframe. For example, the forecasted task data 222 canidentify general demand information regarding expected demand for alltasks, as well as more precise information regarding expected tasks of aspecific type, for a specific patient (e.g., patient John Andrews isexpected to need a re-op procedure tomorrow around 3:00 pm), a specifictime or time frame expected for the task, the expected duration of thetask, expected location of the task, and the like.

The one or more task performance tracking systems 224 can includevarious electronic tracking systems that monitor and/or generate trackedtask performance data 226 regarding progress of performance of reportedand/or scheduled tasks, including information regarding who performsthem, where they are performed and performance quality. For example, theone or more task performance tracking systems 224 can include a tasktracking system that receives user feedback provided the respectivehealthcare workers reporting what they are doing, including informationreporting when they initiate a task and when they complete the task. Insome implementations, the task tracking system can also determine and/orprovide information tracking the progress of the task to completion(e.g., based on an expected duration of the task and/or based ontracking performance of known actions/steps associated with the task).In some implementations, the task tracking system can also allow workersto provide same or similar feedback for other workers. Feedback signalscapturing information about the activity of the workers using varioussensors, including (but not limited to) motion sensors, biofeedbacksensors, images, imaging sensors (e.g., live/video, still images) and/oraudio sensors. For example, in some implementations, the sensoryfeedback can be provided by one or more worker monitoring systems 228.The task performance tracking system 224 can further determine what theworkers are doing based on the sensory feedback, including whether theyare performing a task, what task they are performing and the like. Insome embodiments, the task performance tracking system can evaluate thissensory in conjunction with the user provided feedback to facilitatedetermining what the workers are doing, including determining when theworker reported activity contradicts with the sensory feedback and viceversa. This sensory feedback can also facilitate determining when aworker has idle time, including idle time between tasks and/or duringtasks.

The one or more worker monitoring systems(s) 228 can include one or moresystems associated with a healthcare system (e.g., including a singleoperating entity or group of operating entities (of an integratedhealthcare system)) that track and provide dynamic worker data 230regarding the dynamic activity of healthcare workers of the healthcaresystem over the course of operation of the healthcare system. Forexample, one or more worker monitoring systems 228 can include varioussensory tracking systems that monitor and receive feedback signalscapturing information about the activity of the workers using varioussensors, including (but not limited to) motion sensors, biofeedbacksensors, images, imaging sensors (e.g., live/video, still images) and/oraudio sensors. In some implementations, the one or more workermonitoring systems 228 can further determine what the workers are doingbased on the sensory feedback, including whether they are performing atask, what task they are performing and the like. The one or more workermonitoring systems can also include location and/or motion trackingsystems configured to track real-time information regarding currentlocation of the healthcare workers, live movement data regardingmovement of the healthcare workers from one location to another,mobility data regarding mode of transportation/movement (e.g., driving,walking, riding as a passenger, etc.), motion data regarding precisebody motions of the workers, and the like.

The one or more patient monitoring systems 232 can include varioussystems configured to track and monitor dynamic patient data 234information regarding the location and physiological state of patients.For example, the patient monitoring systems can 232 track location andmovement data regarding the real-time location and movement of thepatients. The patient monitoring systems 232 can also include systemsthat monitor and receive real-time physiological data for patientsregarding their current physiological state from one or more biofeedbackdevices and/or audio/visual monitoring devices. For example, the dynamicpatient data 234 can include real-time data regarding monitoredphysiological parameters of the patient, movement of the patient,appearance of the patient, and the like, that can be used to determineif clinical care is necessitated and if so, what healthcare tasks areinvolved (e.g., administering medication, performing a medicalprocedure, helping the patient out of bed, etc.). In another example,the task identification component 304 can evaluate dynamic operatingconditions data 238 regarding current states of medical instruments,supplies, equipment, etc., to determine needed healthcare tasksinvolving the cleaning, restocking, and/or repairing of the medicalinstruments, supplies, and/or equipment

The one or more operating conditions tracking system(s) 236 can includeone or more systems associated with a healthcare system (e.g., includinga single operating entity or group of operating entities (of anintegrated healthcare system)) that track and provide dynamicinformation regarding the current operating conditions of the healthcaresystem. For example, can include a variety of parameters regarding theoperations conditions, context and or state of the dynamic system at agiven point in time that can impact (e.g., either directly orindirectly) if, when, and where tasks are scheduled and who or whom isassigned to the tasks. The dynamic operating conditions data 238 willalso thus vary based on the type of dynamic system evaluated. Forexample, in implementations in which the dynamic system is a hospital,the dynamic operating conditions data 238 can include but is not limitedto: current occupancy levels of the hospital, status of beds at thehospital (e.g., occupied, assigned, clean, dirty, etc.), number ofpatient waiting for beds, predicted wait times for occupied beds (e.g.,determined based on level/type of care needed, recovery time, proceduresscheduled, etc.), estimates of census pressure on source units wherepatients are waiting for beds, locations of the beds (e.g., by medicalunit), and types of the beds. The dynamic operating conditions data 1238can also include information regarding supplies/equipment used inassociation with performance of the tasks, such as supply/equipmentavailability (e.g., available, in-use, out-of-stock,), status (e.g.,clean/dirty, etc.), supply/equipment location, and the like. The dynamicoperating condition data 238 can also include various additional typesof information about the healthcare environment, such as contextualinformation regarding time of day, day of week/year, weather, localizedevents or conditions at the hospital (e.g., emergency or crisisscenarios, disease outbreaks), local events associated high influx ofpatients, etc.), location and status of ambulatory services and thelike.

FIG. 3 presents an example task assessment module 108 that facilitatesdetermining information regarding currently pending and forecastedhealthcare tasks for performance by one or more operating entities of anintegrated healthcare system in accordance with one or more embodimentsof the disclosed subject matter. In various embodiments, the taskassessment module 108 can include task information extraction component302, task identification component 304, task grouping component 308,task ordering component 310, attribute defining component 312, taskstatus monitoring component 314, and task indexing component 316 andtask assessment machine learning component 318. Repetitive descriptionof like elements employed in respective embodiments is omitted for sakeof brevity.

The task assessment module 108 can provide for extracting, evaluatingand indexing task information regarding the various healthcare tasks tobe performed at a healthcare system over a defined, upcoming timeframe.The various healthcare tasks can include known tasks that are scheduledfor performance over the defined, upcoming timeframe or otherwise tasksfor which there is existing knowledge that the task exists and needs tobe (or should be) completed over the upcoming timeframe. These tasks arecollectively referred to herein as “currently pending” tasks. In someembodiments, the various healthcare tasks can also include forecastedhealthcare tasks, which can include tasks that are expected to ariseover the defined, upcoming timeframe.

The defined, upcoming time can include a timeframe from a current pointin time to a specified time in the future, such as the next hour, thenext 6 hours, the next 24 hours, the next 48 hours, the current workday(e.g., till closing time), the next workweek, the next month etc. Thedefined timeframe can vary. For example, with respect to a hospital thatruns 24 hours a day, the relevant defined timeframe can include anhourly timeframe or a 24-hour timeframe. On the other hand, with respectto specialty healthcare provider's physical office that sees patient'sin accordance with a standard workweek schedule (e.g., 9:00 am to 5:00pm, Monday through Friday), the relevant timeframe can include aworkday, work week, or work month timeframe. In this regard, dependingon the type of operating entity and timeframe evaluated, the taskinformation can reflect healthcare tasks that need to be performedimmediately, healthcare tasks that need to be performed over the nexthour, healthcare tasks that need to be performed over the currentworkday, healthcare tasks that need to be performed over the currentworkweek, healthcare tasks that need to be performed over the nextmonth, two months, etc. Also, depending on the type of operating entityand timeframe evaluated, the healthcare tasks can include tasksscheduled for performance as specific points in time (e.g., patientappointments scheduled for specific dates and times), healthcare tasksscheduled and/or requested for performance over a relatively recenttimeframe or window of time (e.g., the next hour, the next 24 hours,between 2:00 pm and 5:00 pm, etc.), healthcare tasks that need to bepreformed as soon as possible (e.g., urgent/critical tasks), and thelike.

Accordingly, in embodiments in which the disclosed techniques areapplied to an integrated healthcare system that includes a plurality ofdifferent operating entities with different operating environments(e.g., hospitals, specialized hospital units, specialized physicianclinics/offices, outpatient facilities, ambulatory services, nursinghome facilities, surgery centers, imaging/diagnostic providers, pharmacyproviders, traveling/in-home patient care, rehabilitation providers,telemedicine providers, etc.), the task information extracted andevaluated for all the different operating entities combined can reflectvarious different types of tasks for performance over various differenttimeframes with various different time constraints associated with therespective tasks. Further, in some embodiments, the task information caninclude tasks to be completed over different timeframes for a sameoperating entity or group of operating entities (e.g., one timeframe canidentify all tasks that need to be completed over the next hour, anothertimeframe can identify all tasks that need to be completed of the next12 hours, another timeframe can identify all tasks that need to becompleted of the next 24 hours, and the like).

The task assessment module 108 can include task information extractioncomponent 302 to extract (or otherwise receive) the task informationfrom the one or more healthcare information system/sources 102 regardingthe various healthcare tasks to be performed at a healthcare system overa defined, upcoming timeframe. For example, with reference again to FIG.2, the task assessment module 108 can extract and/or receive the taskinformation from the one or more task reporting systems 212 (e.g., thenewly reported tasks data 214), the one or more tasks scheduling systems216 (e.g., task scheduling data 218), and/or the one or more taskforecasting systems 220 (e.g., the forecasted task data 222). The taskinformation extraction component 302 can further regularly orcontinuously extract and/or receive the task information over a courseof operation of the healthcare system to account for new tasks thatarise over the course of operation, enabling a real-time understandingof all the tasks to be performed over the defined, upcoming timeframe.

The task identification component 304 can further process theextracted/received task information (e.g., the newly reported tasks data214, the task scheduling data 218, and/or the forecasted task data 222)to identify all (or defined subsets) of the discrete tasks reflected inthe task information for performance over the defined, upcomingtimeframe. For example, the task identification component 304 canevaluate the extracted/received task information in view of the taskdefinitions/requirement data 202 providing task identifiers andassociated definitions for defined discrete healthcare tasks to identifythe defined discrete healthcare tasks included in the extracted/receivedtask information.

In some embodiments, the task identification component 304 can alsoevaluate patient information regarding current patients of a healthcaresystem (e.g., patients that have entered the healthcare system via atleast one entry point) and their medical needs to determine discretetasks associated with each patient to be completed by healthcare workersof the healthcare system over the defined, upcoming timeframe to satisfythe medical needs of the current patients over the defined, upcomingtimeframe. For example, in the embodiment shown, the task identificationcomponent 204 can include care plan breakdown component 306 to evaluatecare plan information 240 for a patient and identify discrete healthcaretasks to be performed for the patient based on the various clinicalactions and activities described/defined in the care pan information240. In this regard, the care plan breakdown component 306 can evaluatecare plan information 240 for the current patients to identify thevarious discrete healthcare tasks to be performed for the patient over acourse of care and/or within the defined upcoming timeframe, such asdiscrete procedures to be performed, medications to be administered,assessments to be made, and the like. In some embodiments, the care planbreakdown component 306 can also access and evaluate clinical order data242 for a patient to identify discrete healthcare tasks for performancefor the patient of the defined, upcoming timeframe.

In some embodiments, in association with identifying discrete healthcaretasks, the task identification component 304 can be configured toidentify discrete healthcare tasks that can be fulfilled by a singlehealthcare worker or group of healthcare workers. This enables discretehealthcare tasks associated with a single patient (or grouped togetherby another aggregation factor) to be distributed to different healthcareworkers (or groups of healthcare workers). For example, some healthcareoperating models often assign healthcare workers to a specific patientfor the course of the patient's care or another defined timeframe. Underthese models, the healthcare worker is generally responsible forperforming all the healthcare tasks associated with that patient.However, in various contexts, another healthcare worker may be moreappropriate for performing one or more of healthcare tasks for thepatient. For example, an alternative healthcare worker may be moreappropriate for performing a particular task associated with the patientbased on the availability of the original healthcare worker and thealternative healthcare worker, the location of the healthcare worker andthe location of the patient, the expertise of the healthcare worker inassociation with performing the particular task, the demand for theoriginal healthcare worker in another capacity, and the like. Thus, insome embodiments, the task identification component 304 and/or the carepath breakdown component 306 can be configured to segment tasksassociated with a single patient into a plurality of discrete tasks thatcan be distributed to different healthcare workers. The taskidentification component 304 can similarly segment tasks generallygrouped for a performance by a single healthcare worker (e.g., tasklocated in a particular location, tasks of a specific type, etc.) intoseparate tasks that can be assigned to different healthcare workers.

In some embodiments, the task identification component 304 can alsoaccess (or receive via the task information extraction component 302)and evaluate dynamic patient data 234 (e.g., provided by one or morepatient monitoring systems 232) and/or dynamic operating conditions data238 (e.g., provided by one or more operating conditions tracking systems236) to identify discrete healthcare tasks for performance over adefined, upcoming timeframe. With these embodiments, the taskidentification component 304 can evaluate the data to uncover discretehealthcare tasks that are needed for performance when the data itselfdoes not explicitly call out the discrete healthcare tasks are needed.In this regard, the task identification component 304 can examinedynamic information the current context and state of the patients andthe current operating context of the healthcare environment to determinewhat needs to be done, when and where, to account for the patient'sneeds. For example, the task identification component 304 can evaluatedynamic patient data 234 in real-time regarding monitored physiologicalparameters of the patient, movement of the patient, appearance of thepatient, and the like, to determine if clinical care is necessitated andif so, what healthcare tasks are involved (e.g., administeringmedication, performing a medical procedure, helping the patient out ofbed, etc.). For instance, the task identification component 304 candetermine, based on monitored physiological data for a patient, thatemergency services are needed for dispatch to the patient and generatetask information that identifies the one or more discrete tasks involvedwith provision of the emergency services to the patient. In anotherexample, the task identification component 304 can evaluate dynamicoperating conditions data 238 regarding current states of medicalinstruments, supplies, equipment, etc., to determine needed healthcaretasks involving the cleaning, restocking, and/or repairing of themedical instruments, supplies, and/or equipment.

As noted above, in various embodiments, the task identificationcomponent 304 can identify all tasks for performance over a definedupcoming timeframe, including currently pending tasks and in someimplementations, forecasted tasks. These tasks can be grouped byoperating entity, location, timeframe (e.g., within the next hour, thenext 24 hours, the next month, etc.), by type, by patient, by prioritylevel, or another suitable attribute. In one or more embodiments, thetask grouping component 308 can perform or facilitate such grouping oftasks. The task grouping component 308 can also provide for groupingtasks for performance by a single healthcare worker or groups ofhealthcare workers. For example, in various embodiments, the taskidentification component 304 can specifically identify discrete tasksthat can be performed by a single healthcare worker or group ofhealthcare workers. In other embodiments, certain discrete tasksidentified by the task identification component 304 can include groupsof two or more tasks that should be and/or are preferred for performanceby a same healthcare worker or group of healthcare workers (e.g., asdefined in the task definitions/requirement data 202, as provided in theregulatory information 208, and/or as determined using machine learningtechniques). With these embodiments, the grouping component 308 can alsoprovide for identifying two or more tasks that should be (or arerecommended to be) performed by a same healthcare worker or group ofhealthcare workers.

For example, in some embodiments, the task definitions/requirement data202 can identify specific tasks (e.g., by their task identifiers) thatshould be (or are preferred to be) performed by a same healthcare workeror group of healthcare workers based on the task definitions/requirementdata 202 and/or the regulatory information 208. In other embodiments,the task grouping component 308 can employ various machine learningtechniques (e.g., provided by the task assessment machine learningcomponent 318) to learn groups of tasks that are best performed by asame healthcare worker or group of healthcare workers, based on analysisof historical data gathered from the healthcare informationsystems/sources 102. This can include for example, two or more tasksthat when performed by a same healthcare worker or group of healthcareworkers, (as opposed to different healthcare workers of teams), resultsin less overall time to complete the tasks, better clinical results,higher quality of care, greater patient satisfaction, fewercomplications, fewer costs, and the like. For example, two or more taskthat may be grouped for performance by a same healthcare worker or groupof healthcare workers can include tasks associated with a sameprocedure, two or more related tasks for performance at a same location,for a same patient and/or over relatively short timeframe, and the like.

The task ordering component 310 can also determine dependencies betweentasks that influence ordering of the healthcare tasks relative to oneanother. For example, certain healthcare tasks associated with a medicalprocedure, course of patient care, and the like, must be performed in asynchronous manner, while others can be performed asynchronously. Inthis regard, the task ordering component 310 can determine orderinginformation for two or more related tasks regarding a required orpreferred order for performing the two or more related tasks. Withreference to FIG. 2, in some implementations, information defining arequired or preferred order for performing two or more related tasks canbe provided in and/or determined by the task definitions/requirementsdata 202, the regulatory information 208, the newly reported tasks data214 (e.g., based on user input in association with reporting the tasksusing a task reporting system of the one or more task reporting systems212), the care plan information 240, and/or the clinical order data 242.With these implementations, the task ordering component 310 candetermine ordering constraints for two or more tasks using these datasources or similar data sources. In other embodiments, the task orderingcomponent 310 can employ various machine learning techniques to learnordering constraints for two or more healthcare tasks (e.g., provided bythe task assessment machine learning component 318) based on analysis ofhistorical performance data gathered from the healthcare informationsystems/sources 102. This can include for example, a specific manner forordering performance of two or more tasks that results in less overalltime to complete the tasks, better clinical results, higher quality ofcare, greater patient satisfaction, fewer complications, fewer costs,and the like.

The attribute defining component 312 can be configured to determineand/or associate attribute information (e.g., metadata) with each (or insome implementations one or more) of the discrete healthcare tasksidentified by the task identification component 304 based on the dynamicoperating data 104 and/or the static/semi-static system data 106provided the one or more healthcare information systems/sources 102. Theattribute information can include various parameters/attributesassociated with the respective healthcare tasks regarding constraints,conditions and/or requirements of the healthcare tasks that caninfluence if the task is performed (e.g., based on authorizationrestrictions, necessity etc.), when the task is performed, where thetask is performed, how the task is performed (e.g., in person, usingtelemedicine), who or whom performs the task (e.g., a person or person),and/or what additional (non-human) resources are used (e.g., supplies,instruments, equipment, technology, etc.). For example, for eachidentified healthcare task (e.g., currently pending and/or forecasted),the attribute defining component 312 can associate a task identifierwith the healthcare task that uniquely identifies the task. Withreference again to FIG. 2, in some embodiments, the attribute definingcomponent 312 can further access the task definitions/requirements data202 to determine and associate attribute information with the respectivetasks identifying the fixed constraints associated with the respectivetasks defined therein. In other embodiments, downstream components canlook up the various fixed requirements associated with an identifiedtask in the task definitions/requirements data 202 using the taskidentifier for the task.

The attribute defining component 312 can further determine (e.g., usingthe dynamic operating data 104 and/or the static/semi-static system data106 provided by the one or more healthcare information systems/sources102) and/or associate other attribute information with the identifiedtasks, including but not limited to: location attribute informationregarding one or more locations associated with the task, time attributeinformation regarding timing associated with performance of the task,patient attribute information regarding one or more patients associatedwith the task, priority attribute information regarding a priority ofthe task, grouping/ordering attribute information regarding one or moregrouping and/or ordering constraints associated with the task, and thelike.

The location attribute information can include location relatedattributes identifying or indicating one or more locations where thetask is required to be performed or can be performed. For example, inimplementations in which the healthcare system includes a plurality ofdifferent operating entities, the location related attributes canidentify the specific operating entity/provider responsible forperforming the task and/or where the task otherwise originated. Thelocation related attributes can also identify or indicate a physicallocation of the operating entity if the operating entity has isassociated with a fixed physical location or area (e.g., determinedusing the system geospatial data 206). In other implementations in whichthe operating entity has several locations, such as several locationswithin a same geographical area (e.g., a city, a state, etc.), a samecomplex (e.g., different buildings associated with a same complex), asame building (e.g., different medical units or areas within ahospital), etc., the location related attributes can identify thespecific location (e.g., the specific building, medical unit, etc.),where the task is required to be performed and/or can be performed. Invarious implementations in which there are several optional locationsfor performing a task, the location attributes can identify thedifferent options. In addition, the location attributes can identifywhether a location for performing the task is fixed (e.g., cannot bechanged, based on regulatory requirements, system requirements, taskconstraints, a priority or urgency level associated with the task,patient preferences/needs, etc.) or flexible.

In some implementations, a healthcare task can involve a plurality oflocations, such as a task that involving the transportation of a patientand/or supplies, tools, equipment, etc. With these implementations, thelocation attribute information can identify the different locationsinvolved, including start and end locations. In other implementations, ahealthcare task can involve a location external to an operating entity,such as offsite locations where patients are located (e.g., accidentsites, home locations, etc.) where the task involves traveling to thepatient. With these implementations, the location attributes canidentify the location of the patient and/or whether the patient needstransportation to a destination location (e.g., an emergency room of alocal hospital). For example, the patient location can be reported inthe newly reported task data 124, in the task scheduling data 218 and/orthe dynamic patient data 234. Still in other implementations in which atask involves a non-admitted patient, such as a task involvingscheduling a patient appointment for a procedure with a specialist,scheduling the patient for an assessment, scheduling the patient for amedical imaging study, etc., the location attributes can identify a homelocation, a preferred location and/or geographic area of the patientthat is a suitable or preferred location for scheduling the appointment(e.g., determined using the EHR data 244 and/or the patient preferencesdata 248). The location attributes can also include patient preferencesfor locations at which the patient prefers to receive medical services,including specific locations for specific types of medical services(e.g., determined using the patient preferences data 248). Still inother implementations, a healthcare task can include a telemedicine taskwherein the location of the task can be anywhere a healthcare worker canperform the telemedicine task using a suitable electronic communicationdevice.

The time attribute information can include (but is not limited to)attributes identifying or indicating: a time of origination of ahealthcare task, a specific time or timeframe for completing the task(e.g., asap, once every 3 hours, by the end of the day, before 3:00 pm,between noon and 8:00 pm, between April 16^(th) and May 10^(th), etc.),and expected duration of the task or time to complete the task. In someimplementations, the timing attribute information associated with a taskcan be based on patient preference information associated with certaintasks indicating preferred or required timing for performance of thetasks (e.g., provided in the patient preferences data 248, provided withpatient-initiated requests for clinical services included in the newlyreported tasks data 214, etc.). The timing attribute informationassociated with a task can also include information regarding preferredor required patient rest periods when certain tasks should not beperformed. In some implementations, the timing attributes can includeinformation indicating whether the time constraint associated with thetask is fixed of flexible. For example, a low priority task scheduledfor completion by the end of the day may be classified as flexible,indicating that it can be pushed to the next day without resulting insignificant complications or losses. On the other hand, a scheduledappointment for a high priority procedure at specific point in time maybe classified as fixed, indicating that the procedure cannot berescheduled or moved.

The patient attribute information can identify one or more patientsassociated with the task. For example, if a task involves providing aclinical (or non-clinical) service to a patient, the task attributeinformation can identify the specific patient involved (e.g., by name,or another patient identifier). For example, the specific patientassociated with a task can influence who is assigned to the task basedon previous history with the patient and the clinician, based on patientneeds and medical history (e.g., identified in the care plan information240, the clinical order data 242, the EHR data 244, and/or the dynamicpatient data 234), based on patient scheduling and availability (e.g.,provided in the schedule/availability data 246), based on patientpreferences (e.g., provided in the patient preferences data 248 and/orleaned using machine learning techniques), based on patient insuranceinformation 250 and the like). The specific patient associated with atask can also influence when the task is performed and/or where the taskis performed based on same or similar criteria/parameters. In someimplementations, the patient attributed specific information can alsoinclude information regarding a priority status of the patient,including information regarding how the healthcare provider will bereimbursed for the provision for performance of the task (or tasksassociated with the patient), such as insurance information andinformation regarding whether the patient will pay a premium forreceiving priority care and/or care in accordance with specifiedpreferences (e.g., regarding time, location and healthcareworker/workers providing the service). In some embodiments, the patientattribute information can specifically identify patient preferences(e.g., determined based on the patient preferences data 248, and/orlearned using machine learning techniques) regarding when, where, bywhom and/or how (e.g., in person, via telemedicine, food preferences,etc.) a specific task is performed if variable.

The priority attribute information can include attributes that identifyor indicate a priority level of a task (e.g., critical/urgent, highpriority, low priority, medium priority, etc.). For example, in someimplementations, the attribute defining component 312 can determine thepriority level of a task based on the priority level being specified inthe task definitions/requirement data 202. In some implementations, thetiming attributes for a task can be determined based on the prioritylevel associated with a task. For example, tasks classified as highpriority can mean that the timing for completion of the task is as soonas possible, while tasks classified as low priority tasks can beassigned at time attribute of “end-of-day.”

In some embodiments, the attribute defining component 312 can alsoassociate grouping and/or ordering attributes with two or more tasksregarding the grouping and/or ordering constraints determined by thetask grouping component 308 and/or the task ordering component 310. Insome embodiments, the attribute defining component 312 can alsodetermine and associate resource attributes with the healthcare tasksthat identify or indicate requirements for resources to be used for thetasks, including requirements for healthcare workers authorized toperform the tasks (e.g., determined using the healthcare workerinformation 204) as well as requirements for non-human resources, suchas required medications, medical supplies, devices, equipment,technology and the like for use in association with performing therespective tasks (e.g., determined using the taskdefinitions/requirements data 202 and/or the regulatory information208).

In one or more embodiments, the task indexing component 316 can organizeand index the task information regarding currently pending (and in someimplementations forecasted tasks), grouping constraints associated withthe tasks, ordering constraints associated with the task, and thevarious other attributes associated with the respective tasks (e.g.,those described herein) to generate indexed task data 112. For example,the indexed task data 112 can include information identifying (e.g., viatask identifiers), all healthcare tasks (or a grouped subset) of thehealthcare tasks for performance over a defined upcoming timeframe. Theindexed task data 112 can further include various relevant attributesassociated with each (or in some implementations one or more) indexedtask determined by and/or associated therewith by the task groupingcomponent 308, the task ordering component 310, and/or the attributedefining component discussed above. In this regard, the indexed taskdata can also include or associate various attributes with the tasksthat can influence if, where, when, the tasks are performed and by whom.

FIG. 4 presents example indexed task data 400 that can be generated bythe task assessment module 108 in accordance with one or moreembodiments of the disclosed subject matter. The indexed task data 400includes various task for performance by healthcare workers of ahospital, referred to herein as hospital A. In the embodiment shown, theindexed task data 400 provides a list of discrete tasks to be performedfrom a current point in time (e.g., 9:18 am) to defined future point intime (e.g., midnight, or another designated time). The respective tasksare identified by a unique identifier. For example, in accordance withthis example, the identifiers can indicate a type of the respectivetasks, which include clinical tasks, administrative (admin) tasks,transport tasks, and EVS tasks. For each identified task, if applicable,the indexed task data 400 also identifies the patient associated withthe task, the status of the task (e.g., pending or in-progress), atiming constraint associated with the task, a location constraintassociated with the task, and a priority level associated with the task.In accordance with this example, the timing attributes can include atiming of origination of the task, a scheduling time constraintassociated with the task, and an expected duration the task will take tocomplete.

With reference again to FIG. 3, the task assessment module 108 can alsoinclude task status monitoring component 314 to track informationregarding performance of the tasks over a course of operation of thehealthcare system. For example, with reference to FIGS. 2 and 3, thetask status monitoring component 314 can receive and/or monitor trackedtask performance data 226 that identifies or indicates when currentlypending tasks are initiated and when they are completed. The task statusmonitoring component 314 can further regularly and/or continuouslyupdate the indexed task data 112 in real-time over the course ofoperation the integrated healthcare system to reflect changes to thestatus of the tasks.

Various features and functionalities of the task assessment module 108involve evaluating dynamic operating data 104 in real-time in view ofcoinciding static/semi-static system data to identify pending and/orforecasted healthcare tasks (e.g., by the task identification component304), to determine grouping constraints for two or more of thehealthcare tasks (e.g., by the task grouping component 308), todetermine ordering constraints for two or more healthcare tasks (e.g.,by the task ordering component 310), to determine relevant attributesassociated with the healthcare tasks (e.g., by the attribute definingcomponent 312), and/or to determine a status (e.g., currently pending orin-progress) of the healthcare tasks (e.g., by the task statusmonitoring component 314). In some embodiments, the task assessmentmodule 106 can include task assessment machine learning component 318 tofacilitate determining one or more of these task parameters in real-timeusing various suitable machine learning and/or artificial intelligence(AI)-based schemes.

In some embodiments, these machine learning and/or AI schemes caninvolve the development, training (e.g., by the task assessment machinelearning component 318), and/or application (e.g., by the taskidentification component 304, the task grouping component 308, the taskordering component 310, etc.) of various machine learning models basedon analysis of historical data provided by the one or more healthcareinformation systems/sources 102 regarding the historical operations ofthe healthcare system under various dynamic operatingconditions/contexts. In this regard, in some embodiments, using variousmachine learning techniques, the task assessment machine learningcomponent 318 can develop and/or train one or more machine learningmodels, including one or more task identification models (forapplication by the task identification component 304) configured toevaluate dynamic operating data 104 in real-time in view of coincidingstatic/semi-static system data 106 to identify discrete pending and/orforecasted healthcare tasks for assigning to a single healthcare workeror group of healthcare workers. The one or more machine learning modelscan also include one or more task grouping models (for application bythe task grouping component 308) configured to evaluate dynamicoperating data 104 in real-time in view of coinciding static/semi-staticsystem data 106 to determine grouping constraints for two or more of thehealthcare tasks. The one or more machine learning models can alsoinclude one or more task ordering models (for application by the taskordering component 310) configured to evaluate dynamic operating data104 in real-time in view of coinciding static/semi-static system data106 and determine ordering constraints for two or more healthcare tasks.The one or more machine learning models can also include one or moreattribute extraction models (for application by the attribute definingcomponent 312) configured to evaluate dynamic operating data 104 inreal-time in view of coinciding static/semi-static system data 106 todetermine relevant attributes associated with the healthcare tasks thatcan influence scheduling and assignment of resources to the tasks. Theone or more machine learning models can also include one or more taskstatus models (for application by the task status monitoring component314) configured to evaluate dynamic operating data 104 in real-time inview of coinciding static/semi-static system data 106 to determine astatus (e.g., currently pending or in-progress) of the healthcare tasks.

For example, the task assessment machine learning component 318 canemploy various types of machine learning techniques for learningexplicitly or implicitly how segment care plan information into discretetasks, how to group tasks, and/or how to order tasks via an automaticclassification system and process. Inferring or learning can employ aprobabilistic or statistical-based analysis to infer an action that isto be executed. For example, in some implementations, a support vectormachine (SVM) classifier can be employed. Other learning approaches thatcan be employed by the task assessment machine learning component 318can include usage of neural networks (e.g., including deep neuralnetworks, deep adversarial neural networks, convolutional neuralnetworks, and the like), Bayesian networks, decision trees, a nearestneighbor algorithms, boosting algorithm, gradient boosting algorithms,linear regression algorithms, k-means clustering algorithms, associationrules algorithms, q-learning algorithms, temporal difference algorithm,and probabilistic classification models providing different patterns ofindependence can be employed. Learning as used herein also is inclusiveof statistical regression that is utilized to develop models ofpriority.

As will be readily appreciated from the subject specification, thesubject innovation can employ learning classifiers that are explicitlytrained (e.g., via a generic training data) as well as implicitlytrained (e.g., via observing user behavior, receiving extrinsicinformation) so that the learning classifier is used to automaticallydetermine according to predetermined criteria which action to take. Forexample, SVM's can be configured via a learning or training phase withina learning classifier constructor and feature selection module. Alearning classifier is a function that maps an input attribute vector,k=(k1, k2, k3, k4, kn), to a confidence that the input belongs to alearning class—that is, f(k)=confidence(class).

FIG. 5 presents an example resource assessment module 114 thatfacilitates determining information regarding availability of resourcesof an integrated healthcare system in accordance with one or moreembodiments of the disclosed subject matter. Repetitive description oflike elements employed in respective embodiments is omitted for sake ofbrevity.

In various exemplary embodiments, the resource assessment module 114 canbe configured to determine information regarding the availability ofindividuals (i.e., the healthcare workers) employed by and/or affiliatedwith the integrated healthcare system (or an individual operatingentity) that can be assigned to the various healthcare tasks forperformance by the integrated healthcare system over a defined, upcomingtimeframe. In this regard, in addition to knowing what tasks need to beperformed, where they need to be performed (if a location constrain isinvolved), and when they need to be performed (e.g., if a timeconstraint is involved), in order to determine an optimal schedulingarrangement for the tasks with respect to time and location forperforming the respective tasks, the healthcare delivery optimizationserver device 108 needs to determine who is available to perform thetasks, including when they are or can be available and/or where they areor can be located. With these embodiments, the resource assessmentmodule 114 can determine and/or generate availability information (e.g.,resource availability data 116) regarding the availability of thehealthcare workers to perform the healthcare tasks (e.g., includingcurrently pending and/or forecasted tasks) to facilitate scheduling andassigning the healthcare workers to the tasks.

For example, in various embodiments, the resource availability data 116can include information identifying healthcare workers employed byand/or associated with (e.g., as volunteers, independent contractors,patient assistants, etc.) an operating entity or group of operatingentities. In some implementations, the identified healthcare workers canbe grouped by a suitable aggregation factor. For example, the healthcareworkers can be grouped by operating entity, by location, by job/roletitle, and the like. In another example, the healthcare workersidentified can include only those that are “on-the-clock” for thespecified defined, upcoming timeframe of interest. In someimplementations, the resource availability data 116 can also provide acurrent (e.g., in real-time or substantially real-time) status of therespective healthcare workers (e.g., available, unavailable, idle,on-task, on-break, etc.). In some implementations, if a worker's currentavailability status is unavailable (or idle, on-task, on-break, etc.),the resource availability data 116 can include information thatidentifies or indicates a duration of time until the worker will becomeavailable. The resource availability data 116 can also includeinformation identifying or indicating the availability status of thehealthcare workers over a defined upcoming timeframe. For example, inimplementations in which the healthcare system (or individual operatingentity) schedules a healthcare worker to perform certain healthcaretasks at specific times or timeframes in the future (e.g., scheduledpatient appointments, scheduled procedures, etc.), the task availabilitydata 116 can include information identifying one or more timeslots inwhich the healthcare worker is not performing or scheduled to perform ahealthcare task. This can include available timeslots identified in thehealthcare worker's schedule (e.g., provided in the task scheduling data218), as well as forecasted available timeslots. The resourceavailability data 116 can also include location information for thehealthcare workers (if applicable) identifying current locations of therespective workers and know or forecasted locations where the workerswill be over the defined upcoming timeframe. The resource availabilitydata 116 can also include information regarding a mobility state and/ormode of travel of a worker, such as whether the worker is walking,driving a vehicle, riding as a passenger in a vehicle, flying, etc. Insome implementations, the resource availability data 116 can alsoinclude information identifying or indicating a current physiologicaland/or mental state of the healthcare workers, such as a level offatigue, anxiety, and the like. The resource availability data 116 canalso include information regarding healthcare workers that are on-call(e.g., including scheduling information regarding who is on-call andwhen) or otherwise available if certain criterial is met.

In some embodiments, the resources can also include non-human resourcesassociated with the healthcare tasks, such as supplies, tools,instruments, equipment, technology etc. With these embodiments, theresource availability data 116 can also include information regardingthe availability of these non-human resources. For example, the resourceavailability data 116 can include information identifying the currentstatus of supplies, tools, instruments, equipment, technology etc.(e.g., available, unavailable, broken, dirty, overloaded, low/highbandwidth/power level), the current locations of the supplies, tools,instruments, equipment, etc., the current amount of certain supplies,tools, instruments, equipment, etc., and the like.

FIG. 6 presents example resource availability data 600 that can begenerated by the resource assessment module 114 regarding availabilityof resources of an integrated healthcare system in accordance with oneor more embodiments of the disclosed subject matter. The exampleresource availability data 600 provides current availability informationregarding the current availability (e.g., at current time 9:18 am onApr. 22, 2019) of different healthcare workers at a hospital, hospitalA. In the embodiment shown, the different healthcare workers areidentified via unique identification (ID) numbers, however thehealthcare workers can be identified in various other manners (e.g., byname or the like). The example resource availability data 600 includesinformation identifying the current availability status of therespective healthcare workers, which in this example is eitheravailable, unavailable, or idle. For those healthcare workers that areunavailable or idle, the example resource availability data 600 furtheridentifies an expected duration of time until they will becomeavailable. The example resource availability data 600 also includesinformation identifying an expected duration of time the respectiveworkers will be available (e.g., from 9:18 forward). This can includethe expected duration of time in which the available workers will remainavailable, or the expected duration of time in which the idle orunavailable workers will be available after becoming available. Theexample resource availability data 600 also includes locationinformation for the respective workers. For example, the locationinformation can identify their current locations, the expected locationsat the times they will be available, and/or a start and end destinationfor certain workers that are currently traveling from one location toanother. The example resource availability data 600 can also includemobility state information where appropriate that identifies s a currentmobility state of the healthcare workers.

With reference back to FIG. 5, the resource assessment module 114 caninclude various components to facilitate determining and generating theresource availability data 116, including resource monitoring component502, resource availability analysis component 510, and resourceassessment machine learning component 518.

The resource monitoring component 502 can be configured to extractand/or receive and monitor dynamic operating data 104 provided by theone or more healthcare information systems/sources 102 that providesand/or can be used to determine or infer the resource availability data116. For example, with respect to healthcare workers, the resourcemonitoring component 502 can extract, receive and/or monitor informationprovided by the one or more healthcare information systems/sources 102regarding what the healthcare workers are doing, where they are located,a physiological and/or mental state of the healthcare workers and thelike. In the embodiment shown, the resource monitoring component 502 caninclude worker activity monitoring component 504 and location trackingcomponent 506.

In various embodiments, the worker activity monitoring component 504 canextract and/or monitor worker activity information regarding what thehealthcare workers are doing, including monitoring performance ofhealthcare tasks scheduled for performance over a defined, upcomingtimeframe. The healthcare tasks scheduled for performance can includeknown tasks pending for performance over the defined, upcomingtimeframe, excluding forecasted tasks. For example, the scheduledhealthcare tasks can include various tasks assigned to a healthcareworker for performance within the defined timeframe (e.g., a list oftasks to complete, a schedule of patient appointments, etc.). In otherimplementations, the scheduled healthcare tasks can include unassignedtasks. For example, the scheduled healthcare tasks can include thevarious discrete healthcare tasks identified by the task identificationcomponent 304 for performance of the defined, upcoming timeframe. Inthis regard, the worker activity monitoring component 504 can extractand/or monitor worker activity information that identifies or can beused to determine when a is currently performing a scheduled task,and/or when the worker will complete the task.

For example, with reference again to FIG. 2, in various embodiments,this information can be provided by one or more task performance taskperformance tracking systems 224. For example, as described withreference to FIG. 2, in various embodiments, the one or more taskperformance tracking systems 224 can include a task tracking system thatreceives user feedback provided the respective healthcare workersreporting what they are doing, including information reporting when theyinitiate a task and when they complete the task. In someimplementations, the task tracking system can also determine and/orprovide information tracking the progress of the task to completion(e.g., based on an expected duration of the task and/or based ontracking performance of known actions/steps associated with the task).In some implementations, the task tracking system can also allow workersto provide same or similar feedback for other workers. The one or moretask performance tracking systems 224 can include a system that receivesand monitors sensory feedback signals capturing information about theactivity of the workers using various sensors, including (but notlimited to) motion sensors, biofeedback sensors, images, imaging sensors(e.g., live/video, still images) and/or audio sensors. For example, insome implementations, the sensory feedback can be provided by one ormore worker monitoring systems 228. The task performance tracking systemcan further determine what the workers are doing based on the sensoryfeedback, including whether they are performing a task, what task theyare performing and the like. In some embodiments, the task performancetracking system can evaluate this sensory in conjunction with the userprovided feedback to facilitate determining what the workers are doing,including determining when the worker reported activity contradicts withthe sensory feedback and vice versa. This sensory feedback can alsofacilitate determining when a worker has idle time, including idle timebetween tasks and/or during tasks.

The location tracking component 506 can track (e.g., extract/receive andmonitor) information regarding the locations and movement of therespective healthcare workers. This can include real-time informationregarding current location of the healthcare workers, live movement dataregarding movement of the healthcare workers from one location toanother, mobility data regarding mode of transportation/movement (e.g.,driving, walking, riding as a passenger, etc.), motion data regardingprecise body motions of the workers, and the like. For example, invarious embodiments, the location tracking component 506 can receive andmonitor worker location and movement data provided by one or more workermonitoring systems 226. In some embodiments, the tracked location andmovement data (e.g., provided by the worker monitoring systems 228) canbe used by the task performance tracking systems in association with theother sensory feedback and/or the user feedback to facilitatedetermining when a healthcare worker is performing a scheduled task ornot, when the healthcare worker will become available and the like(e.g., based on movement in and out of known rooms/buildings/areas, etc.In other embodiments, the resource availability analysis component 510can evaluate the explicit user feedback regarding what they are doingand when (provided by one or more task performance tracking systems224), the sensory feedback monitored for the workers (e.g., by the oneor more worker monitoring systems 228) and/or the monitored workerlocation and movement data (e.g., provided by the one or more workermonitoring systems 228) to determine what the healthcare workers aredoing (e.g., performing scheduled tasks or not, traveling, etc.) andwhere they are located over a course of operation of the healthcaresystem.

The supply tracking component 508 can further extract and/or receive andmonitor dynamic operating data 104 regarding the availability andlocation of supplies, tools, equipment, instruments and the like. Invarious embodiments, the supply availability data can be provided byand/or determined by one or more operating conditions tracking systems236. In this regard, the one or more dynamic operating conditionstracking systems 236 can track and provide dynamic operating conditionsdata 238 regarding the current locations of mobile supplies, instrumentsand equipment, the status of various supplies, instruments and equipment(e.g., available, unavailable, clean/dirty, broken, in-repair, beingdelivered, etc.), the available quantities of these resources, and thelike.

The resource availability analysis component 510 can be configured toevaluate the information received/extracted and/or monitored by theresource monitoring component 502 to determine and/or infer the resourceavailability data 116. In one or more embodiments, the resourceavailability analysis component 510 can include known availabilitystatus component 512, forecasted availability status component 514 andidle time component 516. The known availability status component 512 candetermine availability information regarding known times or timeframesover a defined upcoming timeframe in which respective healthcare workersare or will be available or unavailable. This can include a currentpoint in time as well as future points/segments in time within thedefined, upcoming timeframe. In some embodiments, the known availabilitystatus component 512 can determine if a worker is currently performing atask or not (and in some implementations, a specific task) based oncurrent worker activity information extracted/received and/or monitoredby the worker activity monitoring component 504. For example, in someimplementations, the known availability status component 512 candetermine if a worker is currently performing a task or not (and in someimplementations, a specific task) based on extracted/received workeractivity information including explicit feedback provided by a workeridentifying if they are performing a task or not. In otherimplementations, the known availability status component 512 can alsodetermine if a worker is currently performing a task or not based onlearned correlations/patterns in the worker sensory feedback data (e.g.,worker motion data, worker biofeedback data, video data, audio data andthe like) and/or the worker location data that reflect whether a task(and even a specific task) is being performed or not.

The known availability status component 512 can also employ additionalinformation in combination with explicit worker feedback, worker sensorydata and/or worker location/movement data to determine or infer whethera worker is currently performing a task or not. For example, thisadditional information can include scheduling information for the worker(e.g., provided by one or more task scheduling systems 216) identifyingtasks the worker is scheduled to perform, timing for performance of thetasks and/or locations for performance of the tasks. This additionalinformation can also include dynamic patient data 234 (e.g., provided byone or more patient monitoring systems 232) identifying currentlocations and states of patients associated with the scheduled tasks(e.g., if the worker and scheduled patient are at different locations,the scheduled task is probably not being performed unless it is atelemedicine interaction). This additional information can also includeinformation tracked by the supply tracking component 508 regardingavailability of supplies to perform certain tasks (e.g., if a requiredsupply is unavailable to perform a scheduled task, it is likely that thehealthcare worker is not currently performing the task). The knownavailability status component 512 can further determine availabilityinformation regarding known times or timeframes over a defined upcomingtimeframe in which respective healthcare workers are or will beavailable or unavailable based the scheduling information. The knownavailability status component 512 can further determine availabilityinformation regarding a mobility state and/or mode of travel of aworker, such as whether the worker is walking, driving a vehicle, ridingas a passenger in a vehicle, flying, etc., based on the tracked locationand/or motion data associated with the worker. The known availabilitystatus component 512 can also determine availability information for aworker identifying or indicating a current physiological and/or mentalstate of the healthcare workers, such as a level of fatigue, anxiety,and the like, based on biofeedback received by one or more workermonitoring systems 228.

The forecasted ability status component 514 can further evaluate themonitored worker activity information and/or the location/movement datain view of known scheduling information for the workers, the indexedtask data 112 (including known and forecasted tasks for performance),the dynamic operating conditions data 238, as well as various othertypes of dynamic and static/semi-static data provided by the healthcareinformation systems/sources 102) to determine or infer forecastedinformation regarding worker availability. For example, the forecastedinformation can include (but is not limited to), expected durations oftime until unavailable healthcare works will become available to performtasks, expected durations of time the available workers will haveavailable when they become available, forecasted timeslots (e.g.,including expected point in time and duration) in which workers will beavailable, forecasted locations where the workers will be available, andthe like. In some embodiments, the forecasted availability statuscomponent 514 can determine expected times and/or timeslots in which ahealthcare worker will be available (or unavailable) based ondetermining estimated arrival times of the healthcare works to tasklocations (e.g., using worker location and/or motion trackinginformation). For example, the forecasted availability status component514 can determine or infer, based on a worker's current location,distance to a task location, mode of travel, traffic, etc., when aworker can arrive at a task location. According to this example, thetravel time/distance can reflect the duration of time until the workerwill become available to perform the task.

In one or more additional embodiments, the forecasted availabilitystatus component 514 employ one or more machine learning modelsconfigured to determine or infer the forecasted resource availabilityinformation based on the various parameters monitored by the resourcemonitoring component 502, the indexed task data 112, dynamic operationconditions data 238 and various additional parameters that have beenlearned to influence worker availability provided by the healthcareinformation systems/sources 102. The one or more machine learning modelscan include but are not limited to: neural networks (e.g., includingdeep neural networks, deep adversarial neural networks, convolutionalneural networks, and the like), Bayesian networks, decision trees, anearest neighbor algorithms, boosting algorithm, gradient boostingalgorithms, linear regression algorithms, k-means clustering algorithms,association rules algorithms, q-learning algorithms, temporal differencealgorithm, and probabilistic classification models. In someimplementations of these embodiments, the resource assessment module 114can include resource assessment machine learning component 510 todevelop and train the one or more machine learning models based onhistorical operations data regarding historicalavailability/unavailability data for the workers (or similar workers) inassociation with performance of various healthcare tasks under differentoperating conditions/contexts of the healthcare system. For example,this historical data can include historical data for specific workersregarding their monitored activity in association performing tasks underdifferent operating conditions/context of the healthcare system,including different schedules and scheduled task loads/types of tasks,different occupancy levels, different locations of the tasks, and thelike.

The resource availability analysis component 510 can also include idletime component 516 to specifically identify and/or forecast worker idletime in which a healthcare worker can perform supplementary tasks. Theterm idle time is used herein to broadly refer to one or more segmentsor periods of time in which a healthcare worker can perform one or moresupplementary tasks, such as unscheduled tasks (e.g., tasks on the fly),telemedicine tasks, relatively short tasks, and the like. For example,idle time can include time in which a healthcare worker is betweenscheduled tasks and/or time in which the healthcare worker canmultitask, such as while traveling (e.g., driving, walking, riding as apassenger, etc.), eating lunch, waiting for laboratory results, or thelike, that can be utilized to perform supplementary tasks. In variousembodiments, the supplementary tasks can include telemedicine tasks,such performing video/telephone chats with patients, remotely monitoringpatients, reviewing radiological images, performing remote patientassessments/check-ups, and the like.

With these embodiments, the idle time component 516 can be configuredevaluate the information monitored by the resources monitoring component504 regarding worker activity, worker location and movement data, aswell as the various additional types of dynamic operating data 104 andstatic/semi-static system data 106 described herein (e.g., to identifyand/or predict timeframes/segments of idle time in which a healthcareworker can perform a supplementary healthcare task. For example, basedon analysis of current activity and scheduling information for ahealthcare worker, the idle time component 516 can determine when thehealthcare worker will have idle time between scheduled tasks, time inwhich the worker will be traveling between task locations (e.g.,driving, walking, riding as a passenger, etc.), and/or time in which theworker is otherwise being underutilized or can multi-task. In anotherexample, based on analysis of explicit worker feedback indicating theworker is available or has idle time, sensory feedback indicating theworker has idle time, and/or worker motion/movement data, the idle timecomponent 516 can determine that worker currently has N minutes of idletime. For example, the idle time component 516 can determine that aworker has N minutes of idle time while traveling, while eating lunch,while waiting for test results, while no or few patients are beingadmitted (e.g., idle time of the admittance receptionist, and the like.The idle time component 516 can also employ one or more machine learningtechniques to learn and forecast information regarding available idletime, and/or underutilized time of certain healthcare workers based onanalysis of historical activity data, travel data, tracked taskperformance data and the like under different operatingcontexts/conditions (e.g., regarding scheduling and task load) for thehealthcare worker or similar healthcare workers (e.g., with similar jobtitles, skill levels, location etc.) and different operatingconditions/contexts of the healthcare system. In some implementations,these machine learning techniques can involve forecasting upcomingdemand and expected time in which the healthcare worker will have toperform a supplementary task given the forecasted demand. In someembodiments, the resource assessment module 114 can also evaluateinformation regarding current and forecasted demand on system resources(e.g., workers needed, instruments needed, etc.) in view of the resourceavailability data 116 to determine if and when additional resources needto be activated (e.g., calling in additional workers, brining inadditional supplies, etc.). For example, in some implementations ofthese embodiments, the resource assessment machine learning component518 can also learn what system resources are needed to satisfy currentand/or forecasted system demands. The resource assessment module 114 canfurther determine if the available resources are sufficient given thecurrent and/or forecasted demand.

FIG. 7 illustrates an example task scheduling and resource assignmentoptimization module 118 that facilitates coordinating and optimizingresource utilization and delivery of healthcare services across anintegrated healthcare system in accordance with one or more embodimentsof the disclosed subject matter. In the embodiment shown, taskscheduling and resource assignment optimization module 118 can includetask optimization analysis component 702 and task assignment component714. Repetitive description of like elements employed in respectiveembodiments is omitted for sake of brevity.

The task optimization analysis component 702 can be configured toevaluate the indexed task data 112, the resources availability data 116and other relevant parameters provided by the one or more healthcareinformation sources/systems to determine task scheduling and resourceassignment information 126 regarding how to schedule tasks and assignresources to the tasks for an integrated healthcare system in real-timein a manner that optimizes performance of the integrated healthcaresystem as a whole by coordinating, synchronizing and balancing theperformance goals of the individual operating entities and the needs andpreferences of the patients. In this regard, the task optimizationanalysis component 702 can evaluate a plurality of complex and dynamicvariables for an entire healthcare operating environment (e.g.,including a single operating entity or in integrated group of operatingentities) regarding what needs to be done and when, who is available todo it, and who is the best person/persons to do it, to determine how toschedule performance of the tasks with respect to time and location andhow to assign resources (e.g., workers and optionally non-humanresources) to the tasks in a manner that results in performing the tasksin the most efficient and effective manner, using the right resources atthe right time for the right patient in the right place.

In various embodiments, the task optimization analysis component 702 candetermine how to schedule tasks and assign resources (e.g., healthcareworkers, medical supplies, instruments, equipment, etc.) to tasks (e.g.,currently pending tasks or scheduled tasks and optimally forecastedtasks) based on task variables provided with the indexed task data 112(or information provided by the healthcare information systems/sources102), and resource availability variables provided by the resourceviability data 116 (or information provided by the healthcareinformation systems/sources 102). For example, the task variables orattributes including (but not limited to): task type and/or definedrequirements/constraints for a specific task, task location variables(e.g., identifying one more locations for performance of the task,classifying a task location as fixed or variable, identifying whetherthe task can be performed remotely using telecommunication, identifyingpatient preferences regarding the task location, identifying start andend locations associated with a task that involves traveling,identifying or indicating a distance associated with the traveling,etc.), task time variables (e.g., identifying or indicated a fixed orvariable time or timeframe for completing a task, identifying orindicating an expected duration of the task, etc.), task groupingconstraints, task ordering constraints, task priority level, patientpreference associated with the tasks (e.g., regarding location, timing,ordering grouping, etc.), and the like. The resource availabilityvariables can include information regarding resource status (e.g.,available, unavailable, idle, etc.), known and expected time ortimeframes when the resources will be available/unavailable, locationand/or movement of the resources, fatigue level, and the like. In someembodiments, the task optimization analysis component 702 can alsofactor in expected/forecasted need of certain workers, balancing costsassociated with assigning them to a certain task now or waiting toassign them to a more demanding or appropriate task expected to arise inthe near future

The task optimization analysis component 702 can also determine how toschedule tasks and assign healthcare workers to the tasks based onhealthcare worker information 204 regarding capabilities,qualifications, preferences, etc., for the respective healthcare workersthat can be assigned to the healthcare tasks. For example, with respectto assigning workers to healthcare tasks, in addition to determining whoto assign to tasks based on availability of the healthcare workers, thetask optimization analysis component 702 can employ workercapability/qualification information to match individual workers withspecific tasks that they are capable/qualified to perform. The taskoptimization analysis component 702 can also examine a pool of availableand qualified workers to further assign the “best” workers to therespective tasks in a manner that maximizes utilization of the availableworkers based on their different qualifications, skill levels,proficiency levels, performance levels, system preference, workerpreference, compensation schedule, patient preferences, and the like.

For example, FIG. 8 presents example healthcare worker information 800(e.g., provided in healthcare worker information 204) that can beapplied by the task optimization analysis component 702 to facilitatesassigning the healthcare workers to healthcare tasks of an integratedhealthcare system in accordance with one or more embodiments of thedisclosed subject matter. In accordance with this example, the examplehealthcare worker information 800 provides task identifiers for thevarious tasks that the healthcare worker (Dr. Keller), is capable and/orqualified to perform. These tasks include clinical procedure tasks andother clinical tasks, as well as administrative and EVS tasks. In thisregard, even though the healthcare worker is a neurological surgeon, theoperating entity (e.g., hospital A) can also use the surgeon to performthe noted administrative and EVS tasks when appropriate. The healthcareworker information also includes a system preference rating thatindicates the system's preference (e.g., the preference of hospital A oranother operating entity associated with hospital A) for using thehealthcare worker for the corresponding task and a worker preferencerating that indicates the worker's preference for performing thecorresponding task (e.g., on a scale of 1-10, wherein 1 is mostpreferred and 10 is least preferred). The example healthcare workerinformation 800 also includes a performance rating that reflects thehistorical performance level of the healthcare worker's performance ofthe corresponding task. The example healthcare worker information 800also indicates the workers specific compensation rate for performing therespective tasks.

With reference again to FIG. 7, the task optimization analysis component702 can employ various machine learning and/or statistical taskoptimization models/algorithms to facilitate determinizing how toschedule tasks and assign resources to the tasks based on the variousparameters/variables described above (e.g., associated with the tasks,the patients associated with the tasks, the healthcare workers thatperform the tasks, the non-human resources needed for the tasks, and insome implementations, the forecasted task demand and resourceavailability). These task optimization models can be configured todetermine an optimal task scheduling and resource assignment schemebased on various optimization criteria, including but not limited to:meeting fixed constraints associated with the tasks (e.g., regardingtiming, location, resource requirements, ordering constraints, groupingconstraints, priority constraints, etc), minimizing delays betweenperformance of the healthcare tasks, ensuring all healthcare tasks aredelivered in accordance with defined quality and regulatoryrequirements, maximizing utilization of available resources, minimizinglosses, maximizing revenue, meeting patient preferences with respect towhen, where and who performs healthcare tasks, and meeting healthcareworker preferences with respect to preferred tasks, timing and locationfor perfuming the tasks. In various embodiments, the task optimizationanalysis component 702 can be configured to employ one or more taskoptimization models (functions, algorithms, etc.) that determines theoptimal task scheduling and resource assignment scheme based on acombination of two or more of these optimization criteria. So forexample, assume the healthcare system has a pool of different workerswith different capabilities, skill levels, salaries, locations, timeavailability, schedules, worker preferences, etc. The healthcare systemalso has many different tasks to be performed by the healthcare workersat any given time or timeframe that vary with respect to type, patient,need, location, time, etc., and wherein new tasks are coming in all thetime. In accordance with this example, the task optimization analysiscomponent 702 can employ one or more optimization models that determinehow to distribute and assign the tasks to the healthcare workers thatresults in getting the tasks done as soon as possible, (by a person thatis qualified to do it), increases patient quality of care, meetsregulatory requirements, minimizes costs (cost attributed to payingworkers and complications), maximizes revenue, and the like.

For example, in some implementations, the task optimization analysiscomponent 702 can employ one or more task optimization models thatschedules tasks (in terms of timing and location) and assign resources(e.g., in terms of which workers, supplies, etc.) to the tasks in anarraignment that results in minimizing delays between origination of atask and initiation or completion of the task. These task optimizationmodels can thus be configured to schedule tasks with respect to time,location and resources to be used for a task that results in getting thetasks completed as fast as possible, considering fixed constraints(e.g., regarding timing, location, resources, regulatory restrictions,quality control requirements, priority constraints, orderingconstraints, etc.) and in some implementations, constraints regardingpatient preferences. For example, these task optimization models canfavor assigning tasks to healthcare workers that are available now (orthe sooner than other healthcare workers) and/or that can travel to thetask location in the shortest time, healthcare workers that arehistorically faster at performing the particular task than otheravailable healthcare worker, and the like.

In another example, the task optimization analysis comment 702 candetermine an optimal scheduling arrangement for the respective taskswith respect to timing of performance and location of performance, andwho or whom to assign to the respective tasks that results in completingall urgent tasks as soon as possible, completing non-urgent tasks attimes and locations preferred by the patients associated with the tasks,meeting defined levels of quality of care and/or regulatory requirementsfor the tasks, and minimizing costs associated with completing all thetasks.

In other embodiments, the task optimization analysis component 702 canemploy one or more task optimization models/functions that scheduletasks (in terms of timing and location) and assign resources (e.g., interms of which workers, supplies, etc.) to the tasks in an arraignmentthat results in maximizing utilization of available resources. Theseresource utilization optimization models can thus be configured todetermine, based on the system needs (e.g., the tasks to be performed)how to assign the available healthcare workers in the most efficient andeffective manner given the system need and theirqualifications/capabilities, locations, etc. For example, in accordancewith these embodiments, the one or more task optimization models can beconfigured to optimize utilization of available resource by minimizingamounts of time in which healthcare workers are not performing tasks, bymaximizing the amount of time the healthcare workers perform tasks theyare most proficient in, by maximizing revenue, by minimizing losses,etc. In some implementations of these embodiments, the task optimizationmodel can be configured to assign task to healthcare workers to minimizetime differences between expected task duration and worker timeavailability duration with a defined buffer of time for task transitions(which can be based on travel time between task). For example, if aworker has an available timeframe or timeslot of 60 minutes, the taskoptimization model can be configured to identify and assign a task tothe worker for performance within the 60-minute timeslot that has anexpected duration of about 55 minutes. In this regard, these taskoptimization models can be configured to determine the best possible useof all available healthcare workers based on the system and patientneeds, considering fixed constraints, patient preferences costs, and thelike.

In some embodiments, the task optimization analysis component 702 canemploy one or more optimization models that maximize utilization ofworkers by repurposing uses of the workers in appropriate contextsbeyond their primary role and sharing workers across different operatingentities based on context and need. With these embodiments, theintegrated healthcare system (or individual operating entities) candefine all possible types of healthcare tasks each worker can performbased on their qualifications, capabilities, performance level and thelike. The healthcare tasks can include healthcare tasks that aretraditionally associated with the job title/description, as well asthose that are outside of their traditional or primary capacity. Forexample, the system can identify a set of primary healthcare tasks of aparticular surgeon included in the integrated healthcare system, such asthe specialized surgery procedures the surgeon is capable of performing.The system can further provide a list of various alternative healthcaretasks the surgeon can perform, including non-surgical tasks, variousgeneral clinical tasks traditionally performed by nurses or lessspecialized clinicians, as well as non-clinical tasks, such asadministrative tasks, EVS tasks, and the like. According to theseembodiments, the task scheduling and resource assignment optimizationmodule 118 can assign healthcare workers to various types of tasks basedon context and need, including tasks that are not their primary rolewhen appropriate to maximize utilization of all workers in everycapacity.

In the embodiment shown, the task optimization analysis component 702includes several components to facilitate determining how to scheduleand assign resources to tasks in accordance with one or moreoptimization models. These components include filtering component 704,task-worker matching component 706, cost analysis component 708, taskcompensation evaluation component 710 and supplemental task selectioncomponent 712.

In various embodiments, the task optimization component 702 can employthe filtering component 704 to facilitate restricting the pool ofhealthcare workers to assign to pending tasks based on one or morecriteria, such as worker capability/requirements and workeravailability. For example, in some embodiments, the task optimizationanalysis component 702 can evaluate information regarding all tasks or adefined subset of task (e.g., grouped by a suitable grouping criterion,such as operating entity, location, priority level, etc.) pending forcompletion over a defined upcoming timeframe. In some embodiments, thetask optimization analysis component 702 can further identify that havenot been assigned to a healthcare worker or (or group of healthcareworkers) and/or that have otherwise not been scheduled with respect totime, location and/or healthcare worker for performing the task. In thisregard, in some embodiments for each “unassigned/unscheduled” tasks, thefiltering component 704 can select a subset of the healthcare workersthat are qualified/capable to perform the task as determined based ondefined capability/qualification restrictions associated with the taskand worker capability/quality information associated with the respectiveworkers. The task optimization analysis component 702 can furtherrestrict assignment (e.g., using one or more optimization models) of thehealthcare workers to the respective pending tasks based on thosesubsets of qualified/capable workers to perform the respective tasks andadditional criteria/constraints associated with the tasks (e.g.,location constraints, time constraints, etc.), the workers (e.g.,regarding availability, performance rating, system preferences, workerpreferences, etc.), the patients (e.g., patient preferences, and thelike). For example, in some implementations in which the defined workercapability information comprises preference classifications for thedifferent types of healthcare tasks representative of relativepreference for performance of the different types of healthcare tasks bythe respective healthcare workers (e.g., the system preference ratingand the worker preference rating information in FIG. 8), the taskoptimization analysis component 702 can further determine the taskassignment scheme using an optimization function that favors assigninghealthcare workers to tasks for which they have a higher system and/orworker preference rating as opposed to a lower system and/or workerpreference rating.

In another embodiment, the filtering component 704 can evaluateinformation regarding pending tasks for performance over a defined,upcoming timeframe (e.g., known and/or forecasted) that have not beenassigned to healthcare workers. For each identified task, the filteringcomponent 704 can evaluate timing and/or location constraints associatedwith the task regarding when the task needs to be (or is preferred tobe) performed, and/or a location associated with the task. Based onthese time/location constraints associated with the task, in someimplementations, the filtering component 704 can evaluate availabilityinformation for the healthcare workers provided in the resourceavailability data 116 to identify a subset of available workers that areavailable to perform. For example, if the task needs to be formed and/oris preferred to be performed now, the filtering component 704 canidentify a subset of available workers with immediate availability toperform the task now. In another example, if the task needs to be formedand/or is preferred to be performed now at specific location, thefiltering component 104 can identify a subset of workers with immediateavailability to perform the task now at the specific location. Inanother example, if the task needs to be formed and/or is preferred tobe performed at specific point in time or over a specific timeframe, thefiltering component 704 can identify a subset of available workers withavailability to perform the task at the specific point in time or overthe specific timeframe. In yet another implementation, if the task ispreferred to be performed at specific point in time or over a specifictimeframe, the filtering component 704 can identify a subset ofavailable workers with the “best” availability to perform the task atthe specific point in time or over the specific timeframe (e.g., workerswith the closest availability to perform the task. The task optimizationanalysis component 702 can further restrict assignment (e.g., using oneor more optimization models) of the healthcare workers to the respectivepending tasks based on those subsets of available workers to perform therespective tasks and additional criteria/constraints associated with thetasks (e.g., resource requirement constraints, ordering constraints,priority constraints, etc.), the workers (e.g., workerqualifications/performance rating, system preferences, workerpreferences, expected need of the workers, etc.), the patients (e.g.,patient preferences, and the like). In other embodiments, the filteringcomponent 704 can generate a filtered subset of workers to restrictassigning to task based on filtering the initial pool of healthcareworkers by both capability/qualifications criteria and availabilitycriteria.

In another embodiment, the task optimization analysis component 702 canemploy task-worker matching component 706 to facilitate matchinghealthcare workers with task based on the various criteria discussedherein. For example, in various embodiments, the task-worker matchingcomponent 706 can dynamically assign a rank or score to different taskand healthcare worker combinations for pending and optionally forecastedtasks for performance within a defined upcoming timeframe for ahealthcare system based on probability models, inference models,artificial intelligence models, and the like (e.g., a dynamic Bayesiannetwork such as a Hidden Markov Model (HMM) using a Viterbi algorithmand the like). In some implementations, the tasks can be grouped by asuitable grouping criterion (e.g., by operating entity, by location, bytimeframe, by priority level, etc.). In this regard, the task-workermatching component 706 can score task-worker assignment combinations toreflect how well the task-worker assignment combination achieves the oneor more goals of the system (e.g., meeting fixed constraints, minimizingdelay between tasks, optimizing patient flow and care, meeting patientneeds and preferences, minimizing costs, maximizing revenue, etc.).

For example, the task-worker matching component 706 can scoretask-worker assignment options based various criteria, including but notlimited to: whether the he worker has the requiredqualifications/capabilities to perform the task, the specificqualifications held by the worker (e.g., certain tasks can preferworkers with specific qualifications), the system preference rating(e.g., wherein a better preference rating, such a preference rating of 1is weighed greater than lower preference rating, such as 2), the workerpreference rating (e.g., which may hold less weight than the systempreference rating, the worker compensation rate for the task (e.g.,wherein lower compensation rates are favored over higher compensationsrates for certain task to facilitate minimizing costs), and the like.The scoring can also reflect degree of correspondence between workeravailability and time/location constraints associated with the task(e.g., providing a measure of how well the worker can fulfil the timeconstraints based on the worker's availability and current location). Insome implementations of these embodiments, the scoring can score thecombination based on how close the expected task duration matches theexpected worker time availability duration, wherein the lesser thedifference the better the score. The scoring can also reflect degree ofmatch between the healthcare worker and preference of the patientassociated with the task (e.g., regarding healthcare workerdemographics, such as gender, age, language, etc., healthcare workerratings and review, healthcare worker complication rate, and the like).

The task-worker matching component 706 can thus generate and associate ascore with each potential task-worker combination that provides anindication of how well the combination facilitates achieving and/ormeeting one or more optimization goals of the healthcare system. Thetask optimization analysis component can further employ the scores tofacilitate determining how to assign the workers to the tasks. Forexample, in some implementations, the task optimization analysiscomponent 702 can assign the highest scored task-worker combinationtogether. However, in various implementations, the task optimizationcomponent 702 can use the scores as additional input to the one or moretask optimization models to facilitate task scheduling and assignment.In this regard, the task optimization models can look at all the scoresfor all the task-worker combinations as a whole in view of variousadditional parameters associated with coordinating and synchronizing thetasks in terms of timing, location and resources assigned (e.g., asingle worker can be matched to two tasks at the same time but notassigned to two tasks at the same time) to determine a final taskscheduling and resource assignment scheme that satisfies the one or moreoptimization criteria of the system.

In another embodiment, the task optimization analysis component 702 cancost analysis component 708 based on costs associated with differentoptional task scheduling and resource assignment schemes. With theseembodiments, the cost analysis component 708 can also evaluate costsassociated with different task assignment schemes that assign the one ormore healthcare workers to the currently pending tasks in differentmanners. For example, the costs can include financial costs attributedto time delays, attributed to compensation schemes associated with usingcertain workers for certain tasks, attributed to expected clinicalcomplications or losses attributed to using certain workers for certaintasks, costs attributed to failure to meet defined regulatoryrequirements/protocols associated with performing the respective tasks(e.g., by a qualified healthcare worker, in a minimum timeframe, etc.)and the like. The task optimization analysis component 702 can furtherdetermine an optimal task scheduling resource assignment scheme based onone or more of the optional task assignments schemes that minimizes thecosts. In some implementations of these embodiments, the cost evaluationcan involve determining compensation costs associated with using aparticular healthcare worker for a certain task. With theseimplementations, the task compensation evaluation component 710 candetermine compensation costs for providing to the healthcare worker ifthe healthcare worker is assigned to the task based on the task-workercompensation schedule information included in the healthcare workerinformation 204), and the expected duration of the task, qualificationof the healthcare worker, a type of the task, and the like.

In one or more additional embodiments, the task optimization analysiscomponent 702 can include supplemental task selection component 712 tofacilitate selecting supplemental task for performance by one or morehealthcare workers that have idle time, or that are otherwise notassigned to scheduled/pending tasks. For example, the supplemental taskselection component 712 can access one or more supplemental task systems716 that include supplemental tasks data 718 regarding supplemental taskthat can be performed by the healthcare workers. For example, thesupplemental task can include telemedicine tasks, such performingvideo/telephone chats with patients, remotely monitoring patients,reviewing radiological images, annotating medical images, performingremote patient assessments/check-ups, and the like. With theseembodiments, the supplemental task system 716 can include a telemedicinesystem with various remote/cloud-based call centers, servers/providerand/or various local provides with one or more physical operatingcenters.

In this regard, in various implementations, the supplemental taskselection component 712 can identify a timeslot for a healthcare worker(e.g., provided in the resource availability data 116) within thedefined timeframe in which a healthcare worker of the healthcare workersis not performing or scheduled to perform a healthcare task of thehealthcare tasks, and determine a supplemental healthcare task forperformance by the healthcare worker during the timeslot. For example,in some implementations, the timeslot can include a timeslot or timesegment classified as idle time (e.g., when the healthcare worker istraveling, waiting for lab results, between tasks, eating lunch, etc.).In some implementations in which the timeslot comprises a travelingtimeslot over which the healthcare worker will travel from a firstlocation to a second location, the supplemental task component 712 candetermine the supplemental task based on a mode the travel. Thesupplemental task component 712 can also determine the supplemental taskbased on various other worker-task matching criteria used by thetask-worker matching component 706 discussed above.

In some embodiments, the task optimization analysis component 702 canemploy several different scheduling and resource assignment schemes forperforming healthcare tasks using different optimization models that aretargeted to different goals. In this regard, the most efficient andeffective manner for scheduling the tasks and assigning resources to thetasks can vary based on the performance goals, needs and preferences ofthe healthcare system. For example, from the perspective of theoperating entity or entities in embodiments in which the disclosedtechniques are applied to an integrated healthcare system, the optimalscheduling and resource assignment scheme for all (or a grouped subset)of known tasks to be performed (e.g., within a defined, upcomingtimeframe) can be based on achieving and/or balancing one or more of thefollowing goals: minimizing delays between performance of the healthcaretasks, ensuring all healthcare tasks are delivered in accordance withdefined quality and regulatory requirements, maximizing utilization ofavailable resources, minimizing losses, maximizing revenue, meetingpatient preferences with respect to when, where and who performshealthcare tasks, and meeting healthcare worker preferences with respectpreferred tasks, timing and location for perfuming the tasks, and thelike.

In this regard, the optimization and/or statistical model or modelsemployed by the task optimization analysis component 702 can be tailoredbased on the needs and preferences of the specific operating entityand/or integrated healthcare system that employs system 100 tofacilitate optimizing their operations. For example, in someimplementations, the task optimization analysis component 702 candetermine a first task scheduling and resource assignment information126 using a first optimization model configured to determine an optimaltask scheduling and resource assignment scheme that focuses more heavilyon minimizing delays between performance of healthcare tasks. The taskoptimization analysis component 702 can also determine a second taskscheduling and resource assignment information 126 using a secondoptimization model configured to determine an optimal task schedulingand resource assignment scheme using a second optimization modelconfigured to determine an alternative scheme that focuses more heavilyon meeting patient preferences. In some embodiments in which thedisclosed techniques are applied to an integrated healthcare systemincluding two or more different operating entities, the taskoptimization analysis component 702 can employ a task optimization modelthat is configured to balance the needs and preferences of all thedifferent operating entities collectively. For example, the taskoptimization model can evaluate all indexed task data 112 and resourcesavailability data 116 for the operating entities combined and determinehow to schedule and assign resources to the tasks to minimize time totaltime between task origination can completion collectively for alloperating entities, to maximize resource utilization collectively forall entities, etc., With these embodiments, the task optimization modelcan include a shared resources model in which the resources of alloperating entities can be combined and shared, facilitating maximizingresource utilization by coordinating and synchronizing patient andprovider needs. In other embodiments, the task optimization analysiscomponent 702 can employ different task optimization models fordifferent operating entities.

The one or more task optimization models can employ various machinelearning techniques (e.g., developed based on based on analysis ofhistorical operations data regarding historical performance of varioushealthcare tasks by the healthcare workers under different operatingconditions of the healthcare system) and/or statistical techniques tofacilitate determining/inferring the optimal task scheduling andresource assignment information (e.g., SVM classification, neuralnetworks (e.g., including deep neural networks, deep adversarial neuralnetworks, convolutional neural networks, and the like), Bayesiannetworks, decision trees, a nearest neighbor algorithms, boostingalgorithm, gradient boosting algorithms, linear regression algorithms,k-means clustering algorithms, association rules algorithms, q-learningalgorithms, temporal difference algorithm, and probabilisticclassification models providing different patterns of independence, andthe like).

The task assignment component 714 can further generate task schedulingand resource assignment information 126 that can be regularly updated inreal-time regarding the determined optimal scheduling/resourceassignment scheme or schemes for an operating entity and/or integratedhealthcare system. For example, task scheduling and resource assignmentinformation 126 can identify known healthcare task to be performed at acurrent point in time and/or over a defined upcoming timeframe, andinclude information identifying a timing for performance of therespective tasks, a location for performance of the respective tasks,the specific healthcare worker or group of healthcare workers assignedto the task, and in some implementations, specific instruments,supplies, equipment, etc., assigned to the respective tasks. Thehealthcare tasks can include all known healthcare tasks for integratedhealthcare system or subsets of the tasks grouped by a defined groupingcriterion, such as by operating entity, by location, by patient, byhealthcare worker, by timeframe, etc. The task assignment component 714can further provide the task scheduling and resource assignmentinformation 126 to task management administrators and/or the healthcareworkers directly to facilitate performing the healthcare tasks inaccordance with the prescribed optimal scheduling and resourceassignment scheme.

For example, in some embodiments, the task assignment component 714 cangenerate and send a task assignment message to a device associated withthe healthcare worker comprising information that recommends thehealthcare worker perform the supplemental healthcare task during thetimeslot. In some implementations, the a task compensation evaluationcomponent 710 can also determine a monetary compensation value forperformance of the supplemental healthcare task by the healthcare workerbased on a qualification of the healthcare worker, a type of the task,and an expected duration of the task, and the task assignment component714 can include compensation information with the task assignmentmessage comprises information identifying the monetary compensationvalue. In other implementation in which the supplemental task comprisesa telemedicine service, the task assignment component can include a linkassociated with the telemedicine service with the task assignmentmessage, wherein selection of the link facilitates performance of thetelemedicine service using the device. The task assignment component 714can also provide the task scheduling and resource assignment information126 to individual patients to provide a real-time schedule of activitiesfor each patient with anticipated date/time of event and coordinates thesequencing of various activities and services to be rendered (andupdated in real-time).

FIG. 9 illustrates an example, high-level flow diagram of acomputer-implemented process 900 for coordinating and optimizingresource utilization and delivery of healthcare services across anintegrated healthcare system using a machine learning framework, inaccordance with one or more embodiments of the disclosed subject matter.Repetitive description of like elements employed in respectiveembodiments is omitted for sake of brevity.

At 902, a system operatively coupled to a processor (e.g., system 100 orthe like), can receive information identifying currently pendinghealthcare tasks for performance by healthcare workers of a healthcaresystem (e.g., via task information extraction component 302). At 904,the system can monitor activity of the healthcare workers in associationwith operation of the healthcare system (e.g., via worker activitymonitoring component 504 and location tracking component 506). At 906,the system can determine availability information regarding availabilityof respective healthcare workers of the healthcare workers to performthe currently pending tasks based on the activity information (e.g., viaresource availability analysis component 510). At 906, the system candetermine a task assignment scheme that assigns one or more of thehealthcare workers to the currently pending tasks in a manner thatminimizes a total amount of delay between timing of origination of thecurrently pending tasks and timing of initiation of performance of thecurrently pending tasks based on the availability information (e.g., viathe task scheduling and resource assignment optimization module 114).

FIG. 10 illustrates another example, high-level flow diagram of acomputer-implemented process 1000 for coordinating and optimizingresource utilization and delivery of healthcare services across anintegrated healthcare system using a machine learning framework, inaccordance with one or more embodiments of the disclosed subject matter.Repetitive description of like elements employed in respectiveembodiments is omitted for sake of brevity.

At 1002, a system operatively coupled to a processor (e.g., system 100or the like), can receive information identifying currently pendinghealthcare tasks for performance by healthcare workers of a healthcaresystem (e.g., via task information extraction component 302). At 1004,the system can determine a first subset of available healthcare workersof to perform the currently pending healthcare tasks based on monitoringactivity data for the healthcare workers (e.g., using worker activitymonitoring component 504). At 1006, the system can determine a secondsubset of qualified healthcare workers included in the first subset ofavailable healthcare workers based on defined worker capabilityinformation and defined capability requirements of the currently pendinghealthcare tasks. At 1008, the system can determine costs associatedwith different task assignment schemes that assign one or more of thequalified healthcare workers to the currently pending tasks in differentmanners. At 1010, the system can then select one of the task assignmentschemes for implementation based on the one or the task assignmentschemes minimizing the costs.

FIG. 11 illustrates another example, high-level flow diagram of acomputer-implemented process 1100 for coordinating and optimizingresource utilization and delivery of healthcare services across anintegrated healthcare system using a machine learning framework, inaccordance with one or more embodiments of the disclosed subject matter.Repetitive description of like elements employed in respectiveembodiments is omitted for sake of brevity.

At 1102, a system operatively coupled to a processor (e.g., system 100or the like), can monitor activity of healthcare workers of a healthcaresystem over a defined timeframe in association with operation of thehealthcare system, including monitoring performance of healthcare tasksscheduled for performance over the defined timeframe (e.g., using workeractivity monitoring component 504). At 1104, the system can determine,based on the monitoring, a timeslot within the defined timeframe inwhich a healthcare worker of the healthcare workers is not performing orscheduled to perform a healthcare task of the healthcare tasks (e.g.,using resource availability analysis component 510). At 1106, the systemcan further determine a supplemental healthcare task for performance bythe healthcare worker during the timeslot (e.g., using task schedulingand resource assignment optimization module 114).

FIG. 12 illustrates another example, high-level flow diagram of acomputer-implemented process 1200 for coordinating and optimizingresource utilization and delivery of healthcare services across anintegrated healthcare system using a machine learning framework, inaccordance with one or more embodiments of the disclosed subject matter.Repetitive description of like elements employed in respectiveembodiments is omitted for sake of brevity.

At 1202, a system operatively coupled to a processor (e.g., system 100or the like), can monitor activity of healthcare workers of a healthcaresystem over a defined timeframe in association with operation of thehealthcare system, including monitoring performance of healthcare tasksscheduled for performance over the defined timeframe (e.g., using workeractivity monitoring component 504). At 1204, the system can determine,based on the monitoring, a timeslot within the defined timeframe inwhich a healthcare worker of the healthcare workers is not performing orscheduled to perform a healthcare task of the healthcare tasks (e.g.,using resource availability analysis component 510). At 1206, the systemcan determine a telemedicine service for performance by the healthcareworker during the timeslot. At 1208, the system can further send a taskassignment message to a device associated with the healthcare workercomprising information that recommends the healthcare worker perform thetelemedicine service during the timeslot and including a link associatedwith the telemedicine service, wherein selection of the link facilitatesperformance of the telemedicine service using the device (e.g., usingtask scheduling and resource assignment optimization module 114).

One or more embodiments can be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product can include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out one or more aspects of the presentembodiments.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the entity's computer, partly on the entity's computer, as astand-alone software package, partly on the entity's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to theentity's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection can bemade to an external computer (for example, through the Internet using anInternet Service Provider). In some embodiments, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It can be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions can be provided to aprocessor of a general-purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionscan also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

In order to provide additional context for various embodiments describedherein, FIG. 13 and the following discussion are intended to provide abrief, general description of a suitable computing environment 1300 inwhich the various embodiments of the embodiment described herein can beimplemented. While the embodiments have been described above in thegeneral context of computer-executable instructions that can run on oneor more computers, those skilled in the art will recognize that theembodiments can be also implemented in combination with other programmodules and/or as a combination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, Internet of Things (IoT)devices, distributed computing systems, as well as personal computers,hand-held computing devices, microprocessor-based or programmableconsumer electronics, and the like, each of which can be operativelycoupled to one or more associated devices.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media, machine-readable storage media,and/or communications media, which two terms are used herein differentlyfrom one another as follows. Computer-readable storage media ormachine-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media or machine-readablestorage media can be implemented in connection with any method ortechnology for storage of information such as computer-readable ormachine-readable instructions, program modules, structured data orunstructured data.

Computer-readable storage media can include, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD), Blu-ray disc (BD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, solid state drives or other solid statestorage devices, or other tangible and/or non-transitory media which canbe used to store desired information. In this regard, the terms“tangible” or “non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and includes any information deliveryor transport media. The term “modulated data signal” or signals refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference again to FIG. 13, the example environment 1300 forimplementing various embodiments of the aspects described hereinincludes a computer 1302, the computer 1302 including a processing unit1304, a system memory 1306 and a system bus 1308. The system bus 1308couples system components including, but not limited to, the systemmemory 1306 to the processing unit 1304. The processing unit 1304 can beany of various commercially available processors. Dual microprocessorsand other multi-processor architectures can also be employed as theprocessing unit 1304.

The system bus 1308 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1306includes ROM 1310 and RAM 1312. A basic input/output system (BIOS) canbe stored in a non-volatile memory such as ROM, erasable programmableread only memory (EPROM), EEPROM, which BIOS contains the basic routinesthat help to transfer information between elements within the computer1302, such as during startup. The RAM 1312 can also include a high-speedRAM such as static RAM for caching data.

The computer 1302 further includes an internal hard disk drive (HDD)1314 (e.g., EIDE, SATA), one or more external storage devices 1316(e.g., a magnetic floppy disk drive (FDD) 1316, a memory stick or flashdrive reader, a memory card reader, etc.) and an optical disk drive 1320(e.g., which can read or write from a CD-ROM disc, a DVD, a BD, etc.).While the internal HDD 1314 is illustrated as located within thecomputer 1302, the internal HDD 1314 can also be configured for externaluse in a suitable chassis (not shown). Additionally, while not shown inenvironment 1300, a solid state drive (SSD) could be used in additionto, or in place of, an HDD 1314. The HDD 1314, external storagedevice(s) 1316 and optical disk drive 1320 can be connected to thesystem bus 1308 by an HDD interface 1324, an external storage interface1326 and an optical drive interface 1328, respectively. The interface1324 for external drive implementations can include at least one or bothof Universal Serial Bus (USB) and Institute of Electrical andElectronics Engineers (IEEE) 1394 interface technologies. Other externaldrive connection technologies are within contemplation of theembodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1302, the drives andstorage media accommodate the storage of any data in a suitable digitalformat. Although the description of computer-readable storage mediaabove refers to respective types of storage devices, it should beappreciated by those skilled in the art that other types of storagemedia which are readable by a computer, whether presently existing ordeveloped in the future, could also be used in the example operatingenvironment, and further, that any such storage media can containcomputer-executable instructions for performing the methods describedherein.

A number of program modules can be stored in the drives and RAM 1312,including an operating system 1330, one or more application programs1332, other program modules 1334 and program data 1336. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1312. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

Computer 1302 can optionally comprise emulation technologies. Forexample, a hypervisor (not shown) or other intermediary can emulate ahardware environment for operating system 1330, and the emulatedhardware can optionally be different from the hardware illustrated inFIG. 13. In such an embodiment, operating system 1330 can comprise onevirtual machine (VM) of multiple VMs hosted at computer 1302.Furthermore, operating system 1330 can provide runtime environments,such as the Java runtime environment or the .NET framework, forapplications 1332. Runtime environments are consistent executionenvironments that allow applications 1332 to run on any operating systemthat includes the runtime environment. Similarly, operating system 1330can support containers, and applications 1332 can be in the form ofcontainers, which are lightweight, standalone, executable packages ofsoftware that include, e.g., code, runtime, system tools, systemlibraries and settings for an application.

Further, computer 1302 can be enable with a security module, such as atrusted processing module (TPM). For instance with a TPM, bootcomponents hash next in time boot components, and wait for a match ofresults to secured values, before loading a next boot component. Thisprocess can take place at any layer in the code execution stack ofcomputer 1302, e.g., applied at the application execution level or atthe operating system (OS) kernel level, thereby enabling security at anylevel of code execution.

A user can enter commands and information into the computer 1302 throughone or more wired/wireless input devices, e.g., a keyboard 1338, a touchscreen 1340, and a pointing device, such as a mouse 1342. Other inputdevices (not shown) can include a microphone, an infrared (IR) remotecontrol, a radio frequency (RF) remote control, or other remote control,a joystick, a virtual reality controller and/or virtual reality headset,a game pad, a stylus pen, an image input device, e.g., camera(s), agesture sensor input device, a vision movement sensor input device, anemotion or facial detection device, a biometric input device, e.g.,fingerprint or iris scanner, or the like. These and other input devicesare often connected to the processing unit 1304 through an input deviceinterface 1344 that can be coupled to the system bus 1308, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, a BLUETOOTH®interface, etc.

A monitor 1346 or other type of display device can be also connected tothe system bus 1308 via an interface, such as a video adapter 1348. Inaddition to the monitor 1346, a computer typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1302 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1350. The remotecomputer(s) 1350 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1302, although, for purposes of brevity, only a memory/storage device1352 is illustrated. The logical connections depicted includewired/wireless connectivity to a local area network (LAN) 1354 and/orlarger networks, e.g., a wide area network (WAN) 1356. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 1302 can beconnected to the local network 1354 through a wired and/or wirelesscommunication network interface or adapter 1358. The adapter 1358 canfacilitate wired or wireless communication to the LAN 1354, which canalso include a wireless access point (AP) disposed thereon forcommunicating with the adapter 1358 in a wireless mode.

When used in a WAN networking environment, the computer 1302 can includea modem 1360 or can be connected to a communications server on the WAN1356 via other means for establishing communications over the WAN 1356,such as by way of the Internet. The modem 1360, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 1308 via the input device interface 1344. In a networkedenvironment, program modules depicted relative to the computer 1302 orportions thereof, can be stored in the remote memory/storage device1352. It will be appreciated that the network connections shown areexample and other means of establishing a communications link betweenthe computers can be used.

When used in either a LAN or WAN networking environment, the computer1302 can access cloud storage systems or other network-based storagesystems in addition to, or in place of, external storage devices 1316 asdescribed above. Generally, a connection between the computer 1302 and acloud storage system can be established over a LAN 1354 or WAN 1356e.g., by the adapter 1358 or modem 1360, respectively. Upon connectingthe computer 1302 to an associated cloud storage system, the externalstorage interface 1326 can, with the aid of the adapter 1358 and/ormodem 1360, manage storage provided by the cloud storage system as itwould other types of external storage. For instance, the externalstorage interface 1326 can be configured to provide access to cloudstorage sources as if those sources were physically connected to thecomputer 1302.

The computer 1302 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, store shelf, etc.), and telephone. This can include WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration and are intended to be non-limiting. For the avoidanceof doubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as an“example” and/or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of entity equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

What has been described above include mere examples of systems andcomputer-implemented methods. It is, of course, not possible to describeevery conceivable combination of components or computer-implementedmethods for purposes of describing this disclosure, but one of ordinaryskill in the art can recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim. The descriptions of the various embodiments have been presentedfor purposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationscan be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A system, comprising: a memory that storescomputer executable components; and a processor that executes thecomputer executable components stored in the memory, wherein thecomputer executable components comprise: an activity monitoringcomponent that monitors activity of healthcare workers of a healthcaresystem over a defined timeframe in association with operation of thehealthcare system, including monitoring performance of healthcare tasksscheduled for performance over the defined timeframe; an availabilityanalysis component that determines, based on the monitoring, a timeslotwithin the defined timeframe in which a healthcare worker of thehealthcare workers is not performing, anticipated or scheduled toperform a healthcare task of the healthcare tasks; and a taskoptimization analysis component that determines a supplementalhealthcare task for performance by the healthcare worker during thetimeslot.
 2. The system of claim 1, wherein computer executablecomponents further comprise: a task assignment component that generatesand sends a task assignment message to a device associated with thehealthcare worker comprising information that recommends the healthcareworker perform the supplemental healthcare task during the timeslot. 3.The system of claim 2, wherein the computer executable componentsfurther comprise: a task compensation evaluation component thatdetermines a monetary compensation value for performance of thesupplemental healthcare task by the healthcare worker based on aqualification of the healthcare worker, a type of the task, and anexpected duration of the task, and wherein the task assignment messagecomprises information identifying the monetary compensation value. 4.The system of claim 2, wherein the supplemental task comprises atelemedicine service, and wherein the task assignment component includesa link associated with the telemedicine service, wherein selection ofthe link facilitates performance of the telemedicine service using thedevice.
 5. The system of claim 1, wherein the computer executablecomponents further comprise: a location tracking component that receiveslocation and movement data regarding locations and movement of thehealthcare workers in real-time over the defined timeframe, and whereinthe task monitoring component determines the timeslot based on thelocation and movement data.
 6. The system of claim 1, wherein thetimeslot comprises a traveling timeslot over which the healthcare workerwill travel from a first location to a second location, and wherein thetask optimization analysis component determines the supplemental taskbased on a mode the travel.
 7. The system of claim 1, wherein theavailability analysis component further determines, based on themonitoring, one or more segments of idle time associated with a currenthealthcare task of the healthcare tasks that is currently beingperformed by the healthcare worker, and wherein the task optimizationanalysis component determines another supplemental healthcare task forperformance by the healthcare worker during the one or more segments ofidle time.
 8. The system of claim 1, wherein the availability analysiscomponent further determines the timeslot using one or more machinelearning models configured to infer available time of the healthcareworkers based on analysis of historical operations data regardinghistorical performance of various healthcare tasks by the healthcareworkers under different operating conditions of the healthcare system.9. The system of claim 1, wherein the task optimization analysiscomponent determines the supplemental healthcare task using one or moremachine learning task optimization models configured to infer optimaltasks for performance by the healthcare workers based on analysis ofhistorical operations data regarding historical performance of varioushealthcare tasks by the healthcare workers under different operatingconditions of the healthcare system.
 10. The system of claim 1, whereinthe task optimization analysis component determines the supplementalhealthcare task based on a location of the healthcare worker, a durationof the timeslot, and one or more preferences of the healthcare worker.11. The system of claim 1, wherein the task optimization analysiscomponent determines the supplemental healthcare task based on a currentoperating context of the healthcare system.
 12. The system of claim 1,wherein the supplemental healthcare task involves provision ofhealthcare to a patient, and wherein the task optimization analysiscomponent determines the supplemental healthcare task for performance bythe healthcare worker based on one or more preferences of the patientand availability of the patient.
 13. The system of claim 12, wherein theoptimization analysis component determines the patient preferences andthe availability of the patient using one or more machine learningoptimization models configured to infer the patient preferences andavailability based on analysis of historical operations data regardinghistorical performance of various healthcare tasks by the healthcareworkers under different operating conditions of the healthcare system.14. A method comprising: monitoring, by a system operatively coupled toa processor, activity of healthcare workers of a healthcare system overa defined timeframe in association with operation of the healthcaresystem, including monitoring performance of healthcare tasks scheduledfor performance over the defined timeframe; determining, by the systembased on the monitoring, a timeslot within the defined timeframe inwhich a healthcare worker of the healthcare workers is not performing,anticipated to perform, or scheduled to perform a healthcare task of thehealthcare tasks; and determining, by the system, a supplementalhealthcare task for performance by the healthcare worker during thetimeslot.
 15. The method of claim 14, further comprising: sending, bythe system, a task assignment message to a device associated with thehealthcare worker comprising information that recommends the healthcareworker perform the supplemental healthcare task during the timeslot. 16.The method of claim 15, further comprising: determining, by the system,a monetary compensation value for performance of the supplementalhealthcare task by the healthcare worker based on a qualification of thehealthcare worker, a type of the task, and an expected duration of thetask; and including, by the system, information identifying the monetarycompensation value in the task assignment message.
 17. The method ofclaim 15, wherein the supplemental task comprises a telemedicineservice, and wherein the task assignment component includes a linkassociated with the telemedicine service, wherein selection of the linkfacilitates performance of the telemedicine service using the device.18. The method of claim 15, wherein the determining the timeslotcomprises determining the timeslot using one or more machine learningtime optimization models configured to infer available time of thehealthcare workers based on analysis of historical operations dataregarding historical performance of various healthcare tasks by thehealthcare workers under different operating conditions of thehealthcare system.
 19. The method of claim 15, wherein the determiningthe supplemental healthcare task comprises determining the supplementalhealthcare task using one or more machine learning task optimizationmodels configured to infer optimal tasks for performance by thehealthcare workers based on analysis of historical operations dataregarding historical performance of various healthcare tasks by thehealthcare workers under different operating conditions of thehealthcare system.
 20. A machine-readable storage medium, comprisingexecutable instructions that, when executed by a processor, facilitateperformance of operations, comprising: monitoring activity of healthcareworkers of a healthcare system over a defined timeframe in associationwith operation of the healthcare system, including monitoringperformance of healthcare tasks scheduled for performance over thedefined timeframe; determining, based on the monitoring, a timeslotwithin the defined timeframe in which a healthcare worker of thehealthcare workers is not performing or scheduled to perform ahealthcare task of the healthcare tasks; and determining a supplementalhealthcare task for performance by the healthcare worker during thetimeslot.